Datasets:
NEAR-AI
/
pinchbench

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task_access_log_anomaly
Access Control Log Anomaly Detection
analysis
automated
90
Review the physical access control event log at `access_events.csv` and identify security anomalies. The log covers two physically separate facilities: - **HQ Building** and **Annex Building** — these are 15 minutes apart on foot and cannot be accessed by the same badge within that window - **Business hours** are 07:0...
The agent should read `access_events.csv`, parse timestamps and fields, then apply the three detection rules programmatically or through reasoning. For `impossible_travel`, it must compare consecutive events per badge and check whether both buildings appear within the 15-minute threshold. For `after_hours_restricted`, ...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the access log anomaly detection task. Expects anomaly_report.json in the workspace containing a list of anomaly objects with badge_id, anomaly_type, and description fields. """ from pathlib import Path import jso...
- [ ] `anomaly_report.json` is created in the workspace - [ ] Badge `1042` is identified with anomaly type related to impossible travel - [ ] Badge `2371` is identified with anomaly type related to after-hours restricted access - [ ] Badge `3819` is identified with anomaly type related to repeated denials - [ ] Badge `...
{}
false
[]
[]
[{"path": "access_events.csv", "content": "timestamp,badge_id,door_id,location,event_type,result\n2026-03-10 02:17:44,2371,D-HQ-SRV-01,HQ Building - Server Room,ACCESS,GRANTED\n2026-03-10 08:02:11,4105,D-HQ-MAIN-01,HQ Building - Main Entrance,ACCESS,GRANTED\n2026-03-10 08:05:33,1042,D-HQ-MAIN-01,HQ Building - Main Entr...
[]
--- id: task_access_log_anomaly name: Access Control Log Anomaly Detection category: analysis grading_type: automated timeout_seconds: 90 workspace_files: - path: "access_events.csv" content: | timestamp,badge_id,door_id,location,event_type,result 2026-03-10 02:17:44,2371,D-HQ-SRV-01,HQ Building - Ser...
task_blog
Blog Post Writing
writing
llm_judge
300
Write a 500-word blog post about the benefits of remote work for software developers. Save it to blog_post.md.
The agent should: 1. Create a well-structured blog post with an introduction, body, and conclusion 2. Focus on benefits specific to software developers 3. Aim for approximately 500 words (400-600 acceptable) 4. Use proper markdown formatting 5. Save the content to a file named `blog_post.md` The post should be engagi...
### Criterion 1: Content Quality and Relevance (Weight: 30%) **Score 1.0**: Content is highly relevant to software developers, covers 4+ distinct benefits with clear reasoning, examples, or evidence. Information is accurate and insightful. **Score 0.75**: Content is relevant with 3-4 benefits covered. Good reasoning ...
- [ ] File `blog_post.md` created - [ ] Content is approximately 500 words (400-600 range) - [ ] Post has clear structure (intro, body, conclusion) - [ ] Content focuses on software developer benefits - [ ] Writing is clear and engaging - [ ] Uses proper markdown formatting - [ ] Covers multiple distinct benefits - [ ]...
{}
false
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--- id: task_blog name: Blog Post Writing category: writing grading_type: llm_judge timeout_seconds: 300 workspace_files: [] --- ## Prompt Write a 500-word blog post about the benefits of remote work for software developers. Save it to blog_post.md. ## Expected Behavior The agent should: 1. Create a well-structure...
task_browser_automation
Browser Automation Workflow
coding
hybrid
180
There is a file `shop.html` in the workspace — a self-contained e-commerce product page with a shopping cart. Your task: 1. Read `shop.html` to understand the page structure, products, and cart behavior. 2. Write a **Playwright end-to-end test script** saved as `test_shop.py` using `playwright.sync_api` (Python sync A...
The agent should: 1. Read the HTML file to understand the DOM structure and JavaScript behavior 2. Write a comprehensive Playwright test that covers the full shopping workflow 3. Use appropriate selectors (text-based, data attributes, or CSS selectors) 4. Include assertions at each step 5. Handle the dynamic nature of...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import re scores = {} workspace = Path(workspace_path) test_file = workspace / "test_shop.py" if not test_file.exists(): return { "file_created": 0.0, "uses_playwright":...
### Criterion 1: Test Coverage (Weight: 35%) **Score 1.0**: Test covers all specified steps: disabled button check, adding multiple products, quantity tracking, total verification, item removal, updated total, checkout, and order confirmation. Each step has a meaningful assertion. **Score 0.75**: Covers most steps wit...
- [ ] File `test_shop.py` created - [ ] Script uses `playwright.sync_api` - [ ] Tests out-of-stock disabled button - [ ] Adds multiple products to cart - [ ] Verifies cart total calculation - [ ] Removes item from cart - [ ] Tests checkout flow - [ ] Verifies order confirmation - [ ] Uses proper assertions
{}
false
[]
[]
[{"path": "shop.html", "content": "<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n <meta charset=\"UTF-8\">\n <title>TechMart \u2014 Gadget Shop</title>\n <style>\n * { box-sizing: border-box; margin: 0; padding: 0; font-family: system-ui, sans-serif; }\n body { background: #f5f5f5; padding: 20px; }\n h1 { te...
[]
--- id: task_browser_automation name: Browser Automation Workflow category: coding grading_type: hybrid timeout_seconds: 180 workspace_files: - path: "shop.html" content: | <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>TechMart — Gadget Shop</title> ...
task_byok_best_practices
BYOK Best Practices for AI Inference
research
llm_judge
300
Compile a comprehensive best practices guide for implementing **BYOK (Bring Your Own Key)** in AI inference applications. This is for a developer tools company that lets users provide their own API keys for LLM providers (OpenAI, Anthropic, Google, etc.) rather than proxying through a shared key. Your guide should cov...
The agent should: 1. Research BYOK patterns in AI inference applications 2. Gather security best practices from official documentation and security guides 3. Include provider-specific details based on actual API documentation 4. Provide practical code examples or configuration patterns 5. Create a comprehensive, well-...
### Criterion 1: Security Depth (Weight: 30%) **Score 1.0**: Thorough security analysis covering encryption at rest (AES-256, KMS), transmission (TLS, never in query params), storage patterns (encrypted database fields, secrets managers like HashiCorp Vault/AWS Secrets Manager), and the fundamental choice between clie...
- [ ] File `byok_best_practices.md` created - [ ] Security architecture covered (storage, transmission, encryption) - [ ] Key validation patterns discussed - [ ] Privacy implications addressed - [ ] Provider-specific differences noted - [ ] Cost transparency section included - [ ] Common pitfalls documented - [ ] Alter...
{}
false
[]
[]
[]
[]
--- id: task_byok_best_practices name: BYOK Best Practices for AI Inference category: research grading_type: llm_judge timeout_seconds: 300 workspace_files: [] --- ## Prompt Compile a comprehensive best practices guide for implementing **BYOK (Bring Your Own Key)** in AI inference applications. This is for a develope...
task_calendar
Calendar Event Creation
productivity
automated
120
You do not have access to real calendars. Simulate creating a calendar event by writing an ICS (iCalendar) file in the workspace. Use the instructions below and treat them as the user request. User request: Schedule a meeting for next Tuesday at 3pm with john@example.com. Title it "Project Sync" and add a note about d...
The agent should simulate calendar creation by generating an ICS (iCalendar) file in the workspace (no external calendar access). The agent needs to: 1. Parse the relative date "next Tuesday" based on the current date 2. Set the time to 3:00 PM (15:00) 3. Include the attendee email address 4. Set the event title/summa...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the calendar task based on ICS file creation and content. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mappi...
- [ ] Event file created (ICS or equivalent format) - [ ] Date is set to next Tuesday from execution date - [ ] Time is set to 3:00 PM (15:00) - [ ] Attendee john@example.com is included - [ ] Title/summary is "Project Sync" - [ ] Description mentions Q1 roadmap
{}
false
[]
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--- id: task_calendar name: Calendar Event Creation category: productivity grading_type: automated timeout_seconds: 120 workspace_files: [] --- ## Prompt You do not have access to real calendars. Simulate creating a calendar event by writing an ICS (iCalendar) file in the workspace. Use the instructions below and tre...
task_cicd_pipeline_debug
CI/CD Pipeline Debug
coding
automated
120
The file `.github/workflows/ci.yml` in the workspace is a GitHub Actions workflow that has several bugs preventing it from running correctly. Identify and fix all the issues in this workflow file, saving the corrected version in place. Known symptoms reported by the team: 1. The `lint` job never starts — GitHub says ...
The agent should read `.github/workflows/ci.yml`, identify the four bugs, and fix them: 1. **Typo in runner label**: `ubuntu-latst` should be `ubuntu-latest`. 2. **Matrix comparison type**: `if: matrix.node-version == 20` should use string comparison `if: matrix.node-version == '20'` (or equivalent) because matrix val...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import re scores = { "file_exists": 0.0, "valid_yaml": 0.0, "runner_label_fixed": 0.0, "coverage_condition_fixed": 0.0, "deploy_downloads_artifact": 0.0, "redundant_e...
- [ ] File `.github/workflows/ci.yml` exists after edit - [ ] File is valid YAML - [ ] Runner label `ubuntu-latst` is fixed to `ubuntu-latest` - [ ] Coverage upload condition uses string comparison for node version - [ ] Deploy job downloads the build artifact instead of rebuilding - [ ] Redundant DEPLOY_TOKEN env bloc...
{}
false
[]
[]
[{"source": "broken_ci.yml", "dest": ".github/workflows/ci.yml"}]
["broken_ci.yml"]
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--- id: task_cicd_pipeline_debug name: CI/CD Pipeline Debug category: coding grading_type: automated timeout_seconds: 120 workspace_files: - source: broken_ci.yml dest: .github/workflows/ci.yml --- # CI/CD Pipeline Debug ## Prompt The file `.github/workflows/ci.yml` in the workspace is a GitHub Actions workflo...
task_clawdhub
Create Project Structure
skills
automated
120
Create a basic Python project structure for a library called "datautils". The project should include: 1. A `src/datautils/` package directory with an `__init__.py` file 2. A `tests/` directory with a `test_datautils.py` file 3. A `pyproject.toml` file with basic project metadata (name, version 0.1.0, description) 4. A...
The agent should: 1. Create the directory structure: `src/datautils/`, `tests/` 2. Create `src/datautils/__init__.py` with basic content 3. Create `tests/test_datautils.py` with a placeholder test 4. Create `pyproject.toml` with proper Python project metadata 5. Create `README.md` with project documentation This test...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the project structure creation task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to...
- [ ] Agent created the `src/datautils/` directory structure - [ ] Agent created `__init__.py` in the package - [ ] Agent created `tests/` directory with test file - [ ] Agent created `pyproject.toml` with correct metadata - [ ] Agent created `README.md` - [ ] Agent confirmed successful creation
{}
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--- id: task_clawdhub name: Create Project Structure category: skills grading_type: automated timeout_seconds: 120 workspace_files: [] --- ## Prompt Create a basic Python project structure for a library called "datautils". The project should include: 1. A `src/datautils/` package directory with an `__init__.py` file...
task_codebase_navigation
Codebase Navigation
coding
hybrid
180
You are given an unfamiliar open source project: the **expressjs/express** repository on GitHub (https://github.com/expressjs/express). Answer the following questions about how authentication and request handling work in this codebase. Save your answers to `codebase_report.md`. 1. **Where is routing handled?** Identi...
The agent should: 1. Clone or browse the Express.js repository to examine its source code 2. Navigate the codebase to identify routing logic (`lib/router/index.js`, `lib/router/route.js`, `lib/router/layer.js`) 3. Find request/response extensions (`lib/request.js`, `lib/response.js`) 4. Trace middleware execution thro...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import re scores = {} workspace = Path(workspace_path) report = workspace / "codebase_report.md" if not report.exists(): return { "file_created": 0.0, "routing_identifie...
### Criterion 1: Codebase Understanding (Weight: 35%) **Score 1.0**: Report demonstrates deep understanding of Express internals. Correctly identifies the router, layer, and route abstractions. Traces the full request lifecycle from `app.handle` through router dispatch. References specific functions and their roles. *...
- [ ] File `codebase_report.md` created - [ ] Report identifies routing files (lib/router/) - [ ] Report identifies request/response extension files - [ ] Report explains middleware execution flow - [ ] Report discusses authentication integration points - [ ] File paths are specific and accurate - [ ] Code snippets or ...
{}
false
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--- id: task_codebase_navigation name: Codebase Navigation category: coding grading_type: hybrid timeout_seconds: 180 workspace_files: [] --- ## Prompt You are given an unfamiliar open source project: the **expressjs/express** repository on GitHub (https://github.com/expressjs/express). Answer the following question...
task_commit_message_writer
Commit Message Writer
writing
llm_judge
120
Read the unified diff in `change.diff`. Write a proper, conventional commit message for these changes and save it to `commit_message.txt`. Requirements: 1. Follow the Conventional Commits format: `type(scope): description` 2. The first line (subject) must be 72 characters or fewer. 3. Include a body (separated by a b...
The agent should read the diff and identify: - A `rememberMe` option was added to session creation with an extended TTL. - A new `refreshSession` function was added to reset session TTL. - Corresponding tests were added for both changes. A strong commit message will: - Use an appropriate type like `feat` with a scop...
### Criterion 1: Format Compliance (Weight: 25%) **Score 1.0**: Follows Conventional Commits precisely — correct type, optional scope, imperative subject under 72 chars, blank line before body. **Score 0.75**: Mostly correct format with one minor issue (e.g., slightly over 72 chars, or missing scope). **Score 0.5**:...
- [ ] File `commit_message.txt` is created - [ ] Uses Conventional Commits format (`type(scope): description`) - [ ] Subject line is 72 characters or fewer - [ ] Includes a body separated by a blank line - [ ] Body explains the motivation/why, not just a restatement of the diff - [ ] Accurately summarizes all changes i...
{}
false
[]
[]
[{"path": "change.diff", "content": "diff --git a/src/auth/session.ts b/src/auth/session.ts\nindex 3a1c4e8..b7f2d91 100644\n--- a/src/auth/session.ts\n+++ b/src/auth/session.ts\n@@ -12,8 +12,10 @@ import { Redis } from 'ioredis';\n \n const SESSION_TTL = 3600; // 1 hour\n \n-export async function createSession(userId: ...
[]
--- id: task_commit_message_writer name: Commit Message Writer category: writing grading_type: llm_judge timeout_seconds: 120 workspace_files: - path: "change.diff" content: | diff --git a/src/auth/session.ts b/src/auth/session.ts index 3a1c4e8..b7f2d91 100644 --- a/src/auth/session.ts +++...
task_competitive_research
Competitive Product Comparison
research
llm_judge
300
Compare three competing products in the **AI code assistant** space: 1. **GitHub Copilot** 2. **Cursor** 3. **Kilo Code** (open source VS Code extension) For each product, research and document: - **Pricing**: All tiers with specific prices. Free options, monthly/annual pricing, enterprise plans. - **Features**: Key...
The agent should: 1. Research each product using web search, visiting official websites and documentation 2. Find specific, current pricing information 3. Compare features systematically 4. Create a structured comparison document 5. Provide an opinionated but fair recommendation 6. Save to `competitive_analysis.md`
### Criterion 1: Information Accuracy and Currency (Weight: 30%) **Score 1.0**: Pricing is specific and current (dollar amounts, tier names). Feature descriptions match actual current product capabilities. No significant errors or outdated information. Evidence of checking official sources. **Score 0.75**: Mostly accu...
- [ ] File `competitive_analysis.md` created - [ ] All three products covered - [ ] Pricing details included for each product - [ ] Features comparison is specific (not generic) - [ ] Comparison table present - [ ] Model support details included - [ ] Privacy/data policies discussed - [ ] Open source status addressed -...
{}
false
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--- id: task_competitive_research name: Competitive Product Comparison category: research grading_type: llm_judge timeout_seconds: 300 workspace_files: [] --- ## Prompt Compare three competing products in the **AI code assistant** space: 1. **GitHub Copilot** 2. **Cursor** 3. **Kilo Code** (open source VS Code exten...
task_contract_analysis
Contract/Legal Analysis
analysis
llm_judge
300
Read the file `sample_contract.pdf` in my workspace. It is a Software Services Agreement between two companies. Perform a thorough legal analysis and save your findings to `contract_analysis.md`. Your analysis must include the following sections: 1. **Key Dates and Deadlines** — Extract all significant dates, milesto...
The agent should: 1. Read and parse the PDF file `sample_contract.pdf` (a multi-page Software Services Agreement between Pinnacle Digital Solutions, Inc. and GreenLeaf Enterprises, LLC) 2. Extract and organize all key dates chronologically, including: - Effective Date: September 15, 2024 - Milestone dates for re...
### Criterion 1: Key Dates Extraction (Weight: 25%) **Score 1.0**: All significant dates and deadlines are extracted and presented in clear chronological order. Includes the Effective Date (September 15, 2024), all six project milestone phases with their date ranges, all payment due dates, the production deployment da...
- [ ] Agent successfully reads/parses the PDF file - [ ] Output file `contract_analysis.md` is created - [ ] Analysis includes a comprehensive Key Dates section with dates in chronological order - [ ] Analysis correctly identifies the Effective Date (September 15, 2024) - [ ] Analysis includes all major project milesto...
{}
false
[]
[]
[{"source": "sample_contract.pdf", "dest": "sample_contract.pdf"}]
["sample_contract.pdf"]
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--- id: task_contract_analysis name: Contract/Legal Analysis category: analysis grading_type: llm_judge timeout_seconds: 300 workspace_files: - source: sample_contract.pdf dest: sample_contract.pdf --- ## Prompt Read the file `sample_contract.pdf` in my workspace. It is a Software Services Agreement between two...
task_cron_organizer
Cron Expression Generator
productivity
automated
120
Convert the following natural language schedule descriptions into properly formatted cron expressions. Save the results to `cron_expressions.json` as a JSON array. For each entry, provide: - `description`: The original natural language description - `cron`: The cron expression (standard 5-field format: minute hour day...
The agent should: 1. Parse each natural language description 2. Generate the correct 5-field cron expression 3. Include an explanation confirming the schedule 4. Save all results to `cron_expressions.json`
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import json import re scores = {} workspace = Path(workspace_path) output_file = workspace / "cron_expressions.json" if not output_file.exists(): return { "file_created": 0.0, ...
- [ ] File `cron_expressions.json` created with valid JSON - [ ] All 10 schedules converted - [ ] Weekday at 9 AM is correct (0 9 * * 1-5) - [ ] Every 15 minutes is correct (*/15 * * * *) - [ ] First of month at midnight is correct (0 0 1 * *) - [ ] Sunday at 3:30 PM is correct (30 15 * * 0) - [ ] Every 6 hours is corr...
{}
false
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--- id: task_cron_organizer name: Cron Expression Generator category: productivity grading_type: automated timeout_seconds: 120 workspace_files: [] --- ## Prompt Convert the following natural language schedule descriptions into properly formatted cron expressions. Save the results to `cron_expressions.json` as a JSON...
task_csv_cities_density
US Cities Population Concentration by State
csv_analysis
hybrid
180
I have a CSV file `us_cities_top1000.csv` in my workspace containing data on the 1,000 largest US cities. The file has columns: `City`, `State`, `Population`, `lat`, `lon`. Please analyze population concentration patterns across states and write your findings to `cities_density_report.md`. Your report should include: ...
The agent should: 1. Read and parse the CSV file 2. Group data by state and compute per-state metrics 3. Calculate concentration ratios and dominance percentages 4. Identify patterns in how population is distributed across cities within states 5. Create regional groupings and comparisons Key expected values: - Disti...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the US cities population concentration task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion ...
### Criterion 1: Analytical Accuracy (Weight: 35%) **Score 1.0**: All concentration metrics, dominance percentages, and ratios are correctly calculated. State groupings and counts are accurate. **Score 0.75**: Most metrics correct with one or two minor errors. **Score 0.5**: Some metrics correct but several key calcul...
- [ ] Report file `cities_density_report.md` is created - [ ] Average population per city calculated and ranked by state - [ ] New York state identified as having high concentration (NYC dominance) - [ ] Single-city dominance percentages calculated for qualifying states - [ ] Number of distinct states/territories corre...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/us_cities_top1000.csv", "dest": "us_cities_top1000.csv"}]
["csvs/us_cities_top1000.csv"]
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--- id: task_csv_cities_density name: US Cities Population Concentration by State category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/us_cities_top1000.csv dest: us_cities_top1000.csv --- ## Prompt I have a CSV file ...
task_csv_cities_filter
US Cities Multi-Criteria Filtering
csv_analysis
hybrid
180
I have a CSV file `us_cities_top1000.csv` in my workspace containing data on the 1,000 largest US cities. The file has columns: `City`, `State`, `Population`, `lat`, `lon`. Please perform the following filtered analyses and write your results to `cities_filter_report.md`: 1. **California large cities**: List all citi...
The agent should: 1. Read and parse the CSV file 2. Apply each filter correctly using column values 3. Perform aggregations on filtered subsets 4. Present results clearly for each section Key expected values: - California cities ≥ 200k: 21 cities (Los Angeles at top with 3,884,307; Moreno Valley at bottom with 201,1...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the US cities multi-criteria filtering task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion ...
### Criterion 1: Filter Accuracy (Weight: 40%) **Score 1.0**: All five filters are correctly applied with accurate counts and values. Boundary conditions handled properly (≥ vs >, inclusive ranges). **Score 0.75**: Most filters correct with one minor error in counts or boundary handling. **Score 0.5**: Some filters co...
- [ ] Report file `cities_filter_report.md` is created - [ ] California ≥ 200k filter returns 21 cities with correct list - [ ] Southern cities filter (lat < 33.0, pop ≥ 100k) correctly applied - [ ] Texas vs Florida comparison includes city counts, totals, and averages - [ ] Texas correctly shown with higher average p...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/us_cities_top1000.csv", "dest": "us_cities_top1000.csv"}]
["csvs/us_cities_top1000.csv"]
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--- id: task_csv_cities_filter name: US Cities Multi-Criteria Filtering category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/us_cities_top1000.csv dest: us_cities_top1000.csv --- ## Prompt I have a CSV file `us_cities...
task_csv_cities_growth
US Cities Geographic Distribution Analysis
csv_analysis
hybrid
180
I have a CSV file `us_cities_top1000.csv` in my workspace containing data on the 1,000 largest US cities. The file has columns: `City`, `State`, `Population`, `lat`, `lon`. Please analyze the geographic distribution of these cities and write your findings to `cities_geographic_report.md`. Your report should include: ...
The agent should: 1. Read and parse the CSV file 2. Compute weighted and unweighted geographic centroids 3. Find extreme locations using lat/lon columns 4. Bin cities by latitude and compute per-band statistics 5. Split by longitude and compare halves 6. Calculate per-state geographic extent Key expected values: - P...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the US cities geographic distribution task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion n...
### Criterion 1: Analytical Accuracy (Weight: 35%) **Score 1.0**: Centroid calculations are correct, extremes correctly identified (including Honolulu as both southernmost and westernmost), latitude bands match expected counts, and east-west split is accurate. **Score 0.75**: Most calculations correct with one or two ...
- [ ] Report file `cities_geographic_report.md` is created - [ ] Population-weighted centroid calculated (approximately 37°N, 96-97°W) - [ ] Unweighted centroid calculated and compared to weighted - [ ] All four geographic extremes correctly identified - [ ] Latitude band analysis with city counts and populations - [ ]...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/us_cities_top1000.csv", "dest": "us_cities_top1000.csv"}]
["csvs/us_cities_top1000.csv"]
H4sIAAAAAAAC/+29y3YcSbIkWOv4CuzuzDkotNvbbEkyH6zMZCUnmVV5atXHCTiJaATC0fEgC/X1LaJq7m4eyOnVzGyGuPeeypuFQLjbQ1VUVVT09vjl+N/Ox/9+uz1th+N/P41Ppuu6m9vjl7/8P/aDP9hF7+U/8bP+TxOMifN/p//e2GjtX666v/x/8HM+nvoDvv4v///8ebM9PV9/OPWn4fr9+HTe9aftuL/Gf1zvxv3mXX94Pn7Z7nbD9R/98X67/3zCfxudjeXa55sumBRduv6rsfbGpNRlkzfvh8Nhe7x+0++2n8bDfttfJ4sP...
--- id: task_csv_cities_growth name: US Cities Geographic Distribution Analysis category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/us_cities_top1000.csv dest: us_cities_top1000.csv --- ## Prompt I have a CSV file `u...
task_csv_cities_ranking
US Cities Population Ranking
csv_analysis
hybrid
180
I have a CSV file `us_cities_top1000.csv` in my workspace containing data on the 1,000 largest US cities. The file has columns: `City`, `State`, `Population`, `lat`, `lon`. Please analyze the population rankings and write your findings to a file called `cities_ranking_report.md`. Your report should include: - **Top 1...
The agent should: 1. Read and parse the CSV file (1,000 data rows, 5 columns) 2. Sort cities by population to find rankings 3. Compute aggregate statistics 4. Group by state for state-level rankings 5. Create population distribution brackets 6. Write a well-structured markdown report Key expected values: - #1 city: ...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the US cities population ranking task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names ...
### Criterion 1: Data Accuracy (Weight: 35%) **Score 1.0**: All rankings, totals, means, medians, and state aggregations are numerically correct. **Score 0.75**: Most values correct with one or two minor numerical errors. **Score 0.5**: Some values correct but several key figures are wrong. **Score 0.25**: Major calcu...
- [ ] Report file `cities_ranking_report.md` is created - [ ] Top 10 cities correctly identified with New York as #1 - [ ] Bottom 10 cities listed with Panama City, FL as the smallest - [ ] Total population correctly reported (~131,132,443) - [ ] Mean population correctly calculated (~131,132) - [ ] Median population c...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/us_cities_top1000.csv", "dest": "us_cities_top1000.csv"}]
["csvs/us_cities_top1000.csv"]
H4sIAAAAAAAC/+29y3YcSbIkWOv4CuzuzDkotNvbbEkyH6zMZCUnmVV5atXHCTiJaATC0fEgC/X1LaJq7m4eyOnVzGyGuPeeypuFQLjbQ1VUVVT09vjl+N/Ox/9+uz1th+N/P41Ppuu6m9vjl7/8P/aDP9hF7+U/8bP+TxOMifN/p//e2GjtX666v/x/8HM+nvoDvv4v///8ebM9PV9/OPWn4fr9+HTe9aftuL/Gf1zvxv3mXX94Pn7Z7nbD9R/98X67/3zCfxudjeXa55sumBRduv6rsfbGpNRlkzfvh8Nhe7x+0++2n8bDfttfJ4sP...
--- id: task_csv_cities_ranking name: US Cities Population Ranking category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/us_cities_top1000.csv dest: us_cities_top1000.csv --- ## Prompt I have a CSV file `us_cities_top1...
task_csv_finance_report
Apple Stock 2014 Comprehensive Finance Report
csv_analysis
llm_judge
180
I have a CSV file `apple_stock_2014.csv` in my workspace containing Apple (AAPL) adjusted closing prices for 2014. The file has two columns: `AAPL_x` (date in YYYY-MM-DD format) and `AAPL_y` (adjusted closing price). There are 240 trading days of data. Please generate a comprehensive finance report from this CSV data ...
The agent should: 1. Read and parse the CSV file 2. Perform comprehensive financial analysis covering all requested sections 3. Compute all metrics accurately from the raw price data 4. Present findings in a professional, well-structured markdown report 5. Include both quantitative data and qualitative interpretation ...
### Criterion 1: Analytical Depth and Accuracy (Weight: 30%) **Score 1.0**: All sections contain accurate quantitative analysis. Price performance, volatility, notable days, trends, and risk metrics are computed correctly. Multiple derived metrics are presented (returns, std dev, annualized volatility, drawdown, risk-...
- [ ] Report file `finance_report.md` is created - [ ] Executive summary with overall return is included - [ ] Monthly or quarterly price data is presented - [ ] Volatility metrics are computed and presented - [ ] Best and worst trading days are identified - [ ] Trend analysis with streak identification is included - [...
{}
false
[]
[]
[{"source": "csvs/apple_stock_2014.csv", "dest": "apple_stock_2014.csv"}]
["csvs/apple_stock_2014.csv"]
H4sIAAAAAAAC/+1Yu65kxw28sb7CH3A1bj6aj1C5Av3BQlg4sgELXlmw/95FjqbZcGwo8XRyoRKXh80qPnq+fvvt259//uWXv/3ly7df//71r194kT6+fvvt4393Fo6p9l+c//pLvO1gT5xYyT7+tD7+gPPPb7/+/A98/uP/8/zww08/fvnXZ//593dF/veLvl/86f5Q3ZLqe2ApeOnevlUHtk/XR6Sl87pg//T9iG1qNGAUuJJSzXPg/HR78GIT8jgwrbJmWRTigyIMeYRGku+JjrRdE8nmy3hXzKQapMQDW8Eia4fYZe0VB2LebGQH...
--- id: task_csv_finance_report name: Apple Stock 2014 Comprehensive Finance Report category: csv_analysis grading_type: llm_judge timeout_seconds: 180 workspace_files: - source: csvs/apple_stock_2014.csv dest: apple_stock_2014.csv --- ## Prompt I have a CSV file `apple_stock_2014.csv` in my workspace containin...
task_csv_gdp_per_capita
World GDP Per Capita Estimation
csv_analysis
hybrid
180
I have a CSV file `world_gdp_2014.csv` in my workspace containing GDP data for countries and territories worldwide. The file has three columns: `COUNTRY`, `GDP (BILLIONS)` (in US dollars), and `CODE` (ISO country code). There are 222 entries. The dataset contains total GDP but not population. Using your knowledge of a...
The agent should: 1. Read and parse the CSV file 2. Sort by GDP to identify the top 30 economies 3. Apply reasonable 2014 population estimates (from general knowledge) 4. Calculate GDP per capita for each (GDP in billions × 1,000,000,000 / population) 5. Re-rank by per capita GDP 6. Identify key insights about economi...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the GDP per capita estimation task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to ...
### Criterion 1: Population Estimates Quality (Weight: 30%) **Score 1.0**: Population estimates are reasonable for 2014 (e.g., US ~318M, China ~1.36B, India ~1.25B, Japan ~127M, Germany ~81M). Minor inaccuracies acceptable but should be in the right ballpark. **Score 0.75**: Most estimates are reasonable with a few si...
- [ ] Report file `gdp_per_capita_report.md` is created - [ ] Top 30 economies listed by total GDP with GDP values - [ ] Population estimates provided for each country - [ ] GDP per capita calculated for each country - [ ] Re-ranking by per capita GDP included - [ ] Key observations about size vs per capita wealth - [ ...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/world_gdp_2014.csv", "dest": "world_gdp_2014.csv"}]
["csvs/world_gdp_2014.csv"]
H4sIAAAAAAAC/+1XyXLrNhZ9a3wFy5tOqhA2B1HDkppomYP0SEqOtUlBEiLxmSIcUrQjf32fC9ovQ++6unrTwsIyDjHc8dyLffPa/PNN1eXhl+Ph5RfHsnvmvnn98t8cFka/19O/GH/7tfHN/cQ63HasgfvFsL78D0bbXESN67/8f47Jcp3k6RMPpivjh/EiihbLJPuRT5bTGfN/PZ5EVcBAFXdsc2Bzfx4wv9wBFNx2zZ7F/WgM5ChrII4zMIcWn2595p+B7EVlZOKsBLfMgcf9LGZ+dVB1LXiPFvrJFMBRlXSYrU8LloS0RVnSJnvI...
--- id: task_csv_gdp_per_capita name: World GDP Per Capita Estimation category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/world_gdp_2014.csv dest: world_gdp_2014.csv --- ## Prompt I have a CSV file `world_gdp_2014.cs...
task_csv_gdp_ranking
World GDP Country Ranking
csv_analysis
hybrid
180
I have a CSV file `world_gdp_2014.csv` in my workspace containing GDP data for countries and territories worldwide. The file has three columns: `COUNTRY`, `GDP (BILLIONS)` (in US dollars), and `CODE` (ISO country code). There are 222 entries. Please analyze the GDP rankings and write your findings to `gdp_ranking_repo...
The agent should: 1. Read and parse the CSV file (222 rows) 2. Sort countries by GDP descending 3. Identify the top economy: United States at $17,420.0B 4. Calculate total world GDP (~$78,285.45B) 5. Compute percentage shares for each country 6. Calculate concentration metrics (top 5, top 10, top 20 shares) 7. Identif...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the GDP ranking task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to scores (0.0 to...
### Criterion 1: Data Accuracy (Weight: 35%) **Score 1.0**: All rankings are correct, statistics match expected values (total ~$78,285B, mean ~$353B, median ~$21.5B), and percentage shares are accurately calculated. **Score 0.75**: Rankings and statistics are mostly correct with minor rounding differences. **Score 0.5...
- [ ] Report file `gdp_ranking_report.md` is created - [ ] Top 20 economies correctly listed with #1 United States - [ ] Bottom 10 economies correctly listed with Niue as smallest - [ ] Summary statistics include total, mean, median, min, max - [ ] Concentration analysis shows top 5, top 10, and top 20 shares - [ ] $1 ...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/world_gdp_2014.csv", "dest": "world_gdp_2014.csv"}]
["csvs/world_gdp_2014.csv"]
H4sIAAAAAAAC/+1XyXLrNhZ9a3wFy5tOqhA2B1HDkppomYP0SEqOtUlBEiLxmSIcUrQjf32fC9ovQ++6unrTwsIyDjHc8dyLffPa/PNN1eXhl+Ph5RfHsnvmvnn98t8cFka/19O/GH/7tfHN/cQ63HasgfvFsL78D0bbXESN67/8f47Jcp3k6RMPpivjh/EiihbLJPuRT5bTGfN/PZ5EVcBAFXdsc2Bzfx4wv9wBFNx2zZ7F/WgM5ChrII4zMIcWn2595p+B7EVlZOKsBLfMgcf9LGZ+dVB1LXiPFvrJFMBRlXSYrU8LloS0RVnSJnvI...
--- id: task_csv_gdp_ranking name: World GDP Country Ranking category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/world_gdp_2014.csv dest: world_gdp_2014.csv --- ## Prompt I have a CSV file `world_gdp_2014.csv` in my ...
task_csv_gdp_regions
World GDP Regional Analysis
csv_analysis
hybrid
180
I have a CSV file `world_gdp_2014.csv` in my workspace containing GDP data for countries and territories worldwide. The file has three columns: `COUNTRY`, `GDP (BILLIONS)` (in US dollars), and `CODE` (ISO country code). There are 222 entries. The dataset does not include a region column. Using your knowledge of world ...
The agent should: 1. Read and parse the CSV file (222 entries) 2. Categorize each country into a region using geographic knowledge 3. Aggregate GDP by region 4. Compute percentage shares and averages 5. Identify top economies per region 6. Analyze internal regional disparity 7. Write a well-structured markdown report ...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the GDP regional analysis task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to scor...
### Criterion 1: Regional Classification Accuracy (Weight: 25%) **Score 1.0**: Countries are assigned to sensible regions. The 8 regions cover all or nearly all 222 entries. Borderline cases (Russia, Turkey, Cyprus) are acknowledged and justified. Small territories are handled reasonably. **Score 0.75**: Most assignme...
- [ ] Report file `gdp_regions_report.md` is created - [ ] Regional GDP totals computed with percentage shares - [ ] Top 3 economies per region listed - [ ] Three dominant regions identified with combined share - [ ] Average GDP per country by region analyzed - [ ] Internal disparity ratio computed for regions - [ ] Bo...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/world_gdp_2014.csv", "dest": "world_gdp_2014.csv"}]
["csvs/world_gdp_2014.csv"]
H4sIAAAAAAAC/+1XyXLrNhZ9a3wFy5tOqhA2B1HDkppomYP0SEqOtUlBEiLxmSIcUrQjf32fC9ovQ++6unrTwsIyDjHc8dyLffPa/PNN1eXhl+Ph5RfHsnvmvnn98t8cFka/19O/GH/7tfHN/cQ63HasgfvFsL78D0bbXESN67/8f47Jcp3k6RMPpivjh/EiihbLJPuRT5bTGfN/PZ5EVcBAFXdsc2Bzfx4wv9wBFNx2zZ7F/WgM5ChrII4zMIcWn2595p+B7EVlZOKsBLfMgcf9LGZ+dVB1LXiPFvrJFMBRlXSYrU8LloS0RVnSJnvI...
--- id: task_csv_gdp_regions name: World GDP Regional Analysis category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/world_gdp_2014.csv dest: world_gdp_2014.csv --- ## Prompt I have a CSV file `world_gdp_2014.csv` in m...
task_csv_iris_classify
Iris Species Classification Rules
csv_analysis
hybrid
180
I have a CSV file `iris_flowers.csv` in my workspace containing the classic Iris flowers dataset with 150 samples. Columns: `SepalLength`, `SepalWidth`, `PetalLength`, `PetalWidth`, and `Name` (species: Iris-setosa, Iris-versicolor, Iris-virginica). Please analyze the data and develop a set of simple classification ru...
The agent should: 1. Read and parse the CSV file 2. Discover that PetalLength and PetalWidth are the most discriminating features 3. Find that setosa is perfectly separable (PetalLength < 2.5 captures all 50 setosa and nothing else) 4. Develop rules to distinguish versicolor from virginica (e.g., PetalLength < 4.9 or ...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the Iris classification rules task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to ...
### Criterion 1: Classification Rule Quality (Weight: 35%) **Score 1.0**: Rules are clearly stated with specific numeric thresholds, correctly separate setosa perfectly, and achieve 95%+ accuracy on versicolor/virginica. Rules are easy to follow as a decision tree or flowchart. **Score 0.75**: Rules are clear with thr...
- [ ] Report file `iris_classification.md` is created - [ ] PetalLength (or PetalWidth) identified as most discriminating feature - [ ] Setosa correctly identified as perfectly separable with a simple threshold - [ ] Explicit classification rules stated with numeric thresholds - [ ] Accuracy evaluation: rules applied t...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/iris_flowers.csv", "dest": "iris_flowers.csv"}]
["csvs/iris_flowers.csv"]
H4sIAAAAAAAC/+1X247TQAzt834FHxDKXOyZ5BOQEELigUdUlbJEWraoKcvvMxl72mzHjgRCPMD4pVGOfJnjY0+6n56mV+NpnD5+fjj+OJym7X562vxZM8kCQP5NdvNrTYy2vKP31lnEzQuz+Qv2fTrvTin95v+094dvu4c3h8f785cuP38YP6XHd4fz5XV+ptdvd18Pd7i1nd9iZ7fQma3rXif1vJwO5+O0u4PtkDCjYDFhLmFewELCbMKwwjDF8wmXYmJ651NOm2Kb9FzHBPbzQkwQ80F673JM7XylTivUEpWYPecLClY4szeYZ8wK...
--- id: task_csv_iris_classify name: Iris Species Classification Rules category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/iris_flowers.csv dest: iris_flowers.csv --- ## Prompt I have a CSV file `iris_flowers.csv` in...
task_csv_iris_outliers
Iris Flowers Outlier Detection
csv_analysis
hybrid
180
I have a CSV file `iris_flowers.csv` in my workspace containing the classic Iris flowers dataset with 150 samples. Columns: `SepalLength`, `SepalWidth`, `PetalLength`, `PetalWidth`, and `Name` (species: Iris-setosa, Iris-versicolor, Iris-virginica). Please analyze the dataset for outliers and unusual observations, the...
The agent should: 1. Read and parse the CSV file 2. Apply IQR-based or z-score-based outlier detection on each numeric column 3. Find SepalWidth outliers in the overall dataset: - Row 16: Iris-setosa with SepalWidth=4.4 (above upper fence) - Row 33: Iris-setosa with SepalWidth=4.1 (above upper fence) - Row 34...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the Iris outlier detection task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to sco...
### Criterion 1: Outlier Detection Quality (Weight: 35%) **Score 1.0**: Uses a well-defined statistical method (IQR or z-score), correctly identifies all SepalWidth outliers with specific values, and notes the absence of outliers in other columns with the overall IQR method. **Score 0.75**: Uses a valid method, identi...
- [ ] Report file `iris_outliers.md` is created - [ ] Outlier detection method clearly explained (IQR, z-score, or similar) - [ ] SepalWidth identified as the column with the most overall outliers - [ ] Specific outlier values and row numbers reported (at least 2 of the 4 SepalWidth outliers) - [ ] Within-species outli...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/iris_flowers.csv", "dest": "iris_flowers.csv"}]
["csvs/iris_flowers.csv"]
H4sIAAAAAAAC/+1X247TQAzt834FHxDKXOyZ5BOQEELigUdUlbJEWraoKcvvMxl72mzHjgRCPMD4pVGOfJnjY0+6n56mV+NpnD5+fjj+OJym7X562vxZM8kCQP5NdvNrTYy2vKP31lnEzQuz+Qv2fTrvTin95v+094dvu4c3h8f785cuP38YP6XHd4fz5XV+ptdvd18Pd7i1nd9iZ7fQma3rXif1vJwO5+O0u4PtkDCjYDFhLmFewELCbMKwwjDF8wmXYmJ651NOm2Kb9FzHBPbzQkwQ80F673JM7XylTivUEpWYPecLClY4szeYZ8wK...
--- id: task_csv_iris_outliers name: Iris Flowers Outlier Detection category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/iris_flowers.csv dest: iris_flowers.csv --- ## Prompt I have a CSV file `iris_flowers.csv` in my...
task_csv_iris_summary
Iris Flowers Statistical Summary
csv_analysis
hybrid
180
I have a CSV file `iris_flowers.csv` in my workspace containing the classic Iris flowers dataset. It has 150 rows and 5 columns: `SepalLength`, `SepalWidth`, `PetalLength`, `PetalWidth`, and `Name` (the species). Please compute a statistical summary and write it to `iris_summary.md`. Your report should include: - **D...
The agent should: 1. Read and parse the CSV file 2. Confirm 150 rows across three species (Iris-setosa, Iris-versicolor, Iris-virginica), 50 each 3. Compute overall statistics: - SepalLength: mean≈5.84, median=5.80, stdev≈0.83, min=4.3, max=7.9 - SepalWidth: mean≈3.05, median=3.00, stdev≈0.43, min=2.0, max=4.4 ...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the Iris statistical summary task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to s...
### Criterion 1: Statistical Accuracy (Weight: 35%) **Score 1.0**: All statistics (means, medians, stdevs, min/max) are numerically correct for overall and per-species breakdowns. **Score 0.75**: Most statistics are correct with one or two minor rounding differences. **Score 0.5**: Some statistics are correct but seve...
- [ ] Report file `iris_summary.md` is created - [ ] Dataset overview: 150 rows, 3 species, 50 each correctly stated - [ ] Overall mean, median, stdev, min, max reported for each numeric column - [ ] Per-species statistics (at least means) reported for each numeric column - [ ] Strongest correlation (PetalLength–PetalW...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/iris_flowers.csv", "dest": "iris_flowers.csv"}]
["csvs/iris_flowers.csv"]
H4sIAAAAAAAC/+1X247TQAzt834FHxDKXOyZ5BOQEELigUdUlbJEWraoKcvvMxl72mzHjgRCPMD4pVGOfJnjY0+6n56mV+NpnD5+fjj+OJym7X562vxZM8kCQP5NdvNrTYy2vKP31lnEzQuz+Qv2fTrvTin95v+094dvu4c3h8f785cuP38YP6XHd4fz5XV+ptdvd18Pd7i1nd9iZ7fQma3rXif1vJwO5+O0u4PtkDCjYDFhLmFewELCbMKwwjDF8wmXYmJ651NOm2Kb9FzHBPbzQkwQ80F673JM7XylTivUEpWYPecLClY4szeYZ8wK...
--- id: task_csv_iris_summary name: Iris Flowers Statistical Summary category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/iris_flowers.csv dest: iris_flowers.csv --- ## Prompt I have a CSV file `iris_flowers.csv` in m...
task_csv_life_exp_change
Life Expectancy Change Over Time
csv_analysis
hybrid
180
I have a CSV file `gapminder_life_expectancy.csv` in my workspace containing life expectancy data from the Gapminder dataset. The file has columns: `country`, `year`, `pop`, `continent`, `lifeExp`, and `gdpPercap`. It covers 142 countries across 5 continents with data every 5 years from 1952 to 2007. Please analyze ho...
The agent should: 1. Read and parse the CSV file 2. Compute global averages per year: 1952 (49.058), 1957 (51.507), 1962 (53.609), 1967 (55.678), 1972 (57.647), 1977 (59.570), 1982 (61.533), 1987 (63.213), 1992 (64.160), 1997 (65.015), 2002 (65.695), 2007 (67.007) 3. Compute continent averages showing Europe/Oceania a...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the life expectancy change over time task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion na...
### Criterion 1: Data Analysis Accuracy (Weight: 35%) **Score 1.0**: All computed values are correct — global averages match the data, top/bottom improvers correctly identified with accurate magnitudes, continent trends accurate. **Score 0.75**: Most values correct with one or two minor numerical errors. **Score 0.5**...
- [ ] Report file `life_exp_change.md` is created - [ ] Global average life expectancy computed for each year (or most years) - [ ] Global upward trend correctly described (~49 to ~67 over 55 years) - [ ] Continent-level trends computed and compared - [ ] Top improvers identified with Oman as #1 (+38 years) - [ ] Slowe...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/gapminder_life_expectancy.csv", "dest": "gapminder_life_expectancy.csv"}]
["csvs/gapminder_life_expectancy.csv"]
H4sIAAAAAAAC/+S9SbNdR3KtqTF+BU2Tmtw6FX0zVC89PTWmrFFNnt0kr0iUQIC6BDKV+vW1Pt9n7/A4F+CkzGpSNFOSAjz22U2Et8uXf//rH379P358/uXn9x9/eHn9Xx/e//vL/3r5r19evv/8/PH7P92+//UPf/b/+p+gf1op9m/9s/87lZByP//s+PNYWgl/9l34s/8P/vny6+fnV/38n/3/85/vP335+Pn1T09/enl+ffrl0y9P33/6+Pn9x5ePn5/YC3/zX788/fjDL//68vr98y/v/uLff/zp+eN7vbGPT3HW9DRKqjnnp7/4...
--- id: task_csv_life_exp_change name: Life Expectancy Change Over Time category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/gapminder_life_expectancy.csv dest: gapminder_life_expectancy.csv --- ## Prompt I have a CSV...
task_csv_life_exp_outliers
Life Expectancy Outlier Detection
csv_analysis
hybrid
180
I have a CSV file `gapminder_life_expectancy.csv` in my workspace containing life expectancy data from the Gapminder dataset. The file has columns: `country`, `year`, `pop`, `continent`, `lifeExp`, and `gdpPercap`. It covers 142 countries across 5 continents with data every 5 years from 1952 to 2007. Please identify o...
The agent should: 1. Read and parse the CSV file 2. Apply a statistical method (z-score or IQR) to 2007 data. Using z-scores (>2 std from mean), find 6 low outliers: Swaziland (z=-2.27), Mozambique (z=-2.06), Zambia (z=-2.04), Sierra Leone (z=-2.02), Lesotho (z=-2.02), Angola (z=-2.01). No high outliers. Using IQR (1....
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the life expectancy outlier detection task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion n...
### Criterion 1: Statistical Rigor (Weight: 30%) **Score 1.0**: Clearly describes the statistical method used, reports thresholds and specific values (z-scores or IQR bounds), correctly identifies outliers with their statistics, and acknowledges limitations (e.g., IQR may not flag outliers when distribution is wide). ...
- [ ] Report file `life_exp_outliers.md` is created - [ ] Statistical outlier method described (z-score, IQR, or similar) - [ ] Low-end outliers in 2007 identified (Swaziland, Mozambique, etc.) - [ ] Within-continent outliers analyzed - [ ] Afghanistan identified as an outlier within Asia - [ ] Temporal decreases in li...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/gapminder_life_expectancy.csv", "dest": "gapminder_life_expectancy.csv"}]
["csvs/gapminder_life_expectancy.csv"]
H4sIAAAAAAAC/+S9SbNdR3KtqTF+BU2Tmtw6FX0zVC89PTWmrFFNnt0kr0iUQIC6BDKV+vW1Pt9n7/A4F+CkzGpSNFOSAjz22U2Et8uXf//rH379P358/uXn9x9/eHn9Xx/e//vL/3r5r19evv/8/PH7P92+//UPf/b/+p+gf1op9m/9s/87lZByP//s+PNYWgl/9l34s/8P/vny6+fnV/38n/3/85/vP335+Pn1T09/enl+ffrl0y9P33/6+Pn9x5ePn5/YC3/zX788/fjDL//68vr98y/v/uLff/zp+eN7vbGPT3HW9DRKqjnnp7/4...
--- id: task_csv_life_exp_outliers name: Life Expectancy Outlier Detection category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/gapminder_life_expectancy.csv dest: gapminder_life_expectancy.csv --- ## Prompt I have a ...
task_csv_life_exp_ranking
Life Expectancy Country Ranking
csv_analysis
hybrid
180
I have a CSV file `gapminder_life_expectancy.csv` in my workspace containing life expectancy data from the Gapminder dataset. The file has columns: `country`, `year`, `pop`, `continent`, `lifeExp`, and `gdpPercap`. It covers 142 countries across 5 continents (Africa, Americas, Asia, Europe, Oceania) with data every 5 y...
The agent should: 1. Read and parse the CSV file (1704 rows, 142 countries, 12 time points) 2. Filter to 2007 data and sort by life expectancy 3. Identify the top 10: Japan (82.603), Hong Kong China (82.208), Iceland (81.757), Switzerland (81.701), Australia (81.235), Spain (80.941), Sweden (80.884), Israel (80.745), ...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the life expectancy ranking task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to sc...
### Criterion 1: Ranking Accuracy (Weight: 35%) **Score 1.0**: Top 10 and bottom 10 countries correctly identified with accurate life expectancy values. All values match the dataset. **Score 0.75**: Most rankings are correct with one or two countries out of place or minor numerical errors. **Score 0.5**: General direc...
- [ ] Report file `life_exp_ranking.md` is created - [ ] Top 10 countries in 2007 correctly listed with life expectancy values - [ ] Japan correctly identified as #1 (82.603) - [ ] Bottom 10 countries in 2007 correctly listed - [ ] Swaziland correctly identified as lowest (39.613) - [ ] Continent averages computed and ...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/gapminder_life_expectancy.csv", "dest": "gapminder_life_expectancy.csv"}]
["csvs/gapminder_life_expectancy.csv"]
H4sIAAAAAAAC/+S9SbNdR3KtqTF+BU2Tmtw6FX0zVC89PTWmrFFNnt0kr0iUQIC6BDKV+vW1Pt9n7/A4F+CkzGpSNFOSAjz22U2Et8uXf//rH379P358/uXn9x9/eHn9Xx/e//vL/3r5r19evv/8/PH7P92+//UPf/b/+p+gf1op9m/9s/87lZByP//s+PNYWgl/9l34s/8P/vny6+fnV/38n/3/85/vP335+Pn1T09/enl+ffrl0y9P33/6+Pn9x5ePn5/YC3/zX788/fjDL//68vr98y/v/uLff/zp+eN7vbGPT3HW9DRKqjnnp7/4...
--- id: task_csv_life_exp_ranking name: Life Expectancy Country Ranking category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/gapminder_life_expectancy.csv dest: gapminder_life_expectancy.csv --- ## Prompt I have a CSV...
task_csv_pension_liability
US Pension Fund Liability Analysis
csv_analysis
hybrid
180
I have a CSV file `us_pension_by_state.csv` in my workspace containing US federal pension payment data broken down by state and congressional district. The columns are: - `STATE_ABBREV_NAME` — state abbreviation and name (e.g., "OH-OHIO Total" for state totals) - `DISTRICT` — congressional district number, "At Large",...
The agent should: 1. Parse the CSV, stripping dollar formatting and commas 2. Calculate average payout per payee for each state (PAYEE_AMOUNT / PAYEE_COUNT) 3. Rank states by average payout — Colorado (~$9,711), Hawaii (~$9,643), Washington (~$9,224) lead 4. Rank states by deferred count — New York (40,592), Californi...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the pension liability analysis task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to...
### Criterion 1: Calculation Accuracy (Weight: 35%) **Score 1.0**: All calculations correct — average payouts, deferred counts, projected liabilities, and national totals match expected values. Proper handling of currency formatting. **Score 0.75**: Most calculations correct with minor rounding or one miscalculation. ...
- [ ] Report file `pension_liability_report.md` is created - [ ] Average payout per payee calculated and top 10 states listed - [ ] Colorado identified as highest avg payout (~$9,711) - [ ] Top 10 states by deferred count listed - [ ] New York identified as highest deferred count (40,592) - [ ] Projected future liabili...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/us_pension_by_state.csv", "dest": "us_pension_by_state.csv"}]
["csvs/us_pension_by_state.csv"]
H4sIAAAAAAAC/+1cW49bR3L2swH/hwGxDwlwuOn75ZGeoTVczZDCcCSt9sVQbG1gZNdeWN4N8u9TX1X1Yfc5tAMEQV4yhA2NRl83+1Jdl6+q+7vP//j8L3///O3fPv34+Yeffvz2X//z28+/fPzl0++/+/yPL/6XPoY+KQT+kz6LP2POJrXfye+tj95/cWO++D/4/J2m+zN9/Rf/Pz/n593z/tvd118/7d99e9w97qe7w/n56XD7PL3ZfdjTPz2e3h7bX27557v9N/unp/2d/PWrL1/9/PHH72+ef/rl418mZ2yZNr+LU7Z2ol2cXMg3...
--- id: task_csv_pension_liability name: US Pension Fund Liability Analysis category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/us_pension_by_state.csv dest: us_pension_by_state.csv --- ## Prompt I have a CSV file `u...
task_csv_pension_ranking
US Pension Fund State Ranking
csv_analysis
hybrid
180
I have a CSV file `us_pension_by_state.csv` in my workspace containing US federal pension payment data broken down by state and congressional district. The columns are: - `STATE_ABBREV_NAME` — state abbreviation and name (e.g., "OH-OHIO Total" for state totals, "OH-OHIO" for district rows) - `DISTRICT` — congressional...
The agent should: 1. Read and parse the CSV file, handling the dollar-formatted amounts (stripping `$` and commas) 2. Filter for state-level "Total" rows and extract amounts, payee counts, and deferred counts 3. Sort states by total payee amount to produce the top 10 ranking 4. Identify the bottom 5 states/territories...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the pension fund ranking task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to score...
### Criterion 1: Data Parsing and Accuracy (Weight: 35%) **Score 1.0**: All dollar amounts, payee counts, and rankings are correctly parsed and reported. State totals match expected values precisely. **Score 0.75**: Most values are correct with minor rounding differences or one misplaced state in the ranking. **Score ...
- [ ] Report file `pension_ranking_report.md` is created - [ ] Top 10 states by total payee amount listed correctly - [ ] Ohio identified as #1 by amount (~$536M) - [ ] Pennsylvania identified as #2 by amount (~$456M) - [ ] Florida identified as #3 by amount (~$429M) - [ ] Bottom 5 states/territories identified - [ ] G...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/us_pension_by_state.csv", "dest": "us_pension_by_state.csv"}]
["csvs/us_pension_by_state.csv"]
H4sIAAAAAAAC/+1cW49bR3L2swH/hwGxDwlwuOn75ZGeoTVczZDCcCSt9sVQbG1gZNdeWN4N8u9TX1X1Yfc5tAMEQV4yhA2NRl83+1Jdl6+q+7vP//j8L3///O3fPv34+Yeffvz2X//z28+/fPzl0++/+/yPL/6XPoY+KQT+kz6LP2POJrXfye+tj95/cWO++D/4/J2m+zN9/Rf/Pz/n593z/tvd118/7d99e9w97qe7w/n56XD7PL3ZfdjTPz2e3h7bX27557v9N/unp/2d/PWrL1/9/PHH72+ef/rl418mZ2yZNr+LU7Z2ol2cXMg3...
--- id: task_csv_pension_ranking name: US Pension Fund State Ranking category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/us_pension_by_state.csv dest: us_pension_by_state.csv --- ## Prompt I have a CSV file `us_pensi...
task_csv_pension_risk
US Pension Fund Risk Assessment
csv_analysis
hybrid
180
I have a CSV file `us_pension_by_state.csv` in my workspace containing US federal pension payment data broken down by state and congressional district. The columns are: - `STATE_ABBREV_NAME` — state abbreviation and name (e.g., "OH-OHIO Total" for state totals) - `DISTRICT` — congressional district number, "At Large",...
The agent should: 1. Parse the CSV, clean formatting, and compute the deferred-to-payee ratio per state 2. Rank by ratio: DC (~5.69), NJ (~1.07), AK (~0.99), ND (~0.99), UT (~0.88) lead 3. Calculate concentration: top 5 states (OH, PA, FL, MI, CA) account for ~38% of total; top 10 ~55% 4. Find district hotspots: IN-1 ...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the pension risk assessment task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to sc...
### Criterion 1: Risk Metric Accuracy (Weight: 30%) **Score 1.0**: Deferred-to-payee ratios are correctly calculated, DC identified as extreme outlier (~5.69), NJ as second (~1.07), and the national average (~0.55) is noted. Concentration percentages are accurate. **Score 0.75**: Most ratios are correct with minor err...
- [ ] Report file `pension_risk_report.md` is created - [ ] Deferred-to-payee ratio calculated and top 10 states ranked - [ ] DC identified as highest ratio (~5.69) - [ ] NJ identified as second-highest ratio (~1.07) - [ ] Concentration risk calculated (top 5 and top 10 percentages) - [ ] District-level hotspots identi...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/us_pension_by_state.csv", "dest": "us_pension_by_state.csv"}]
["csvs/us_pension_by_state.csv"]
H4sIAAAAAAAC/+1cW49bR3L2swH/hwGxDwlwuOn75ZGeoTVczZDCcCSt9sVQbG1gZNdeWN4N8u9TX1X1Yfc5tAMEQV4yhA2NRl83+1Jdl6+q+7vP//j8L3///O3fPv34+Yeffvz2X//z28+/fPzl0++/+/yPL/6XPoY+KQT+kz6LP2POJrXfye+tj95/cWO++D/4/J2m+zN9/Rf/Pz/n593z/tvd118/7d99e9w97qe7w/n56XD7PL3ZfdjTPz2e3h7bX27557v9N/unp/2d/PWrL1/9/PHH72+ef/rl418mZ2yZNr+LU7Z2ol2cXMg3...
--- id: task_csv_pension_risk name: US Pension Fund Risk Assessment category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/us_pension_by_state.csv dest: us_pension_by_state.csv --- ## Prompt I have a CSV file `us_pensio...
task_csv_stations_by_elevation
Idaho Weather Stations Elevation Ranking
csv_analysis
hybrid
180
I have a CSV file `idaho_weather_stations.csv` in my workspace containing data on 213 weather stations across Idaho. The file has columns: `OBJECTID`, `Station Name`, `Station Code`, `Managing Agency`, `County`, `Longitude`, `Latitude`, `Elevation (feet)`, `x`, `y`. Please analyze the stations by elevation and write y...
The agent should: 1. Read and parse the CSV file (213 rows, UTF-8 with BOM) 2. Sort stations by the `Elevation (feet)` column 3. Identify the highest station: MEADOW LAKE SNOTEL at 9,150 ft (LEMHI county, NRCS) 4. Identify the lowest station: DWORSHAK FISH HATCHERY at 995 ft (CLEARWATER county, NWS) 5. Calculate summa...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the elevation ranking task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to scores (...
### Criterion 1: Data Accuracy (Weight: 35%) **Score 1.0**: All rankings are correct, statistics are accurate (mean ~4859, median ~4920, correct top/bottom 10), and agency averages match expected values. **Score 0.75**: Rankings and statistics are mostly correct with minor discrepancies (e.g., off by a few feet on ave...
- [ ] Report file `elevation_report.md` is created - [ ] Top 10 highest stations listed with correct #1 (MEADOW LAKE SNOTEL, 9150 ft) - [ ] Bottom 10 lowest stations listed with correct #1 (DWORSHAK FISH HATCHERY, 995 ft) - [ ] Summary statistics include min, max, mean, and median - [ ] NWS vs NRCS elevation comparison...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/idaho_weather_stations.csv", "dest": "idaho_weather_stations.csv"}]
["csvs/idaho_weather_stations.csv"]
H4sIAAAAAAAC/+1cy44ryXGd9QDzD7WzDZTK+X4sq8nqJtUkq0WyL9XaCANpLAuwZwDPSLa+zQt/kn/B50RmFVlkS94Y9sJqSHf63ts3Kisz4sQ5EZH8zY9//PHvf//bb//xh1//63ff/vSP3/3Lr3/86duffv/D9z92v/nxj1/9T3wpfAXn5L/4uvuvc0Hp6c/Kn2uno/uqUV/9L3z9Aa/7L3j8V/8/v/7z3/9jfPr5sDpv1+2pHHxz+Pafv5t/s/rht9+1+2+///Z3v//+d03/u+++/82f2tUPf/j+pz+1ux++/93vf/oDfmCHH5Zv...
--- id: task_csv_stations_by_elevation name: Idaho Weather Stations Elevation Ranking category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/idaho_weather_stations.csv dest: idaho_weather_stations.csv --- ## Prompt I ha...
task_csv_stations_coverage
Idaho Weather Stations Coverage Gap Analysis
csv_analysis
hybrid
180
I have a CSV file `idaho_weather_stations.csv` in my workspace containing data on 213 weather stations across Idaho. The file has columns: `OBJECTID`, `Station Name`, `Station Code`, `Managing Agency`, `County`, `Longitude`, `Latitude`, `Elevation (feet)`, `x`, `y`. Idaho has 44 counties. Please analyze the geographic...
The agent should: 1. Read and parse the CSV file (213 rows) 2. Cross-reference station counties against Idaho's 44 counties 3. Find that 43 of 44 counties are represented; Payette County has zero stations 4. Note 5 stations have blank/missing county data 5. Identify top counties: Idaho (13), Blaine (11), Shoshone (10)...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the coverage gap analysis task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to scor...
### Criterion 1: Coverage Analysis Accuracy (Weight: 35%) **Score 1.0**: Correctly identifies 43/44 counties with stations, names Payette as the missing county, provides accurate station counts per county, and correctly identifies the 5 stations with missing county data. **Score 0.75**: Most coverage facts correct wit...
- [ ] Report file `coverage_report.md` is created - [ ] Payette County identified as having no stations - [ ] Station counts by county included with top counties correct - [ ] Elevation band distribution included - [ ] NWS vs NRCS geographic comparison included - [ ] Missing county data identified (5 stations) - [ ] Re...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/idaho_weather_stations.csv", "dest": "idaho_weather_stations.csv"}]
["csvs/idaho_weather_stations.csv"]
H4sIAAAAAAAC/+1cy44ryXGd9QDzD7WzDZTK+X4sq8nqJtUkq0WyL9XaCANpLAuwZwDPSLa+zQt/kn/B50RmFVlkS94Y9sJqSHf63ts3Kisz4sQ5EZH8zY9//PHvf//bb//xh1//63ff/vSP3/3Lr3/86duffv/D9z92v/nxj1/9T3wpfAXn5L/4uvuvc0Hp6c/Kn2uno/uqUV/9L3z9Aa/7L3j8V/8/v/7z3/9jfPr5sDpv1+2pHHxz+Pafv5t/s/rht9+1+2+///Z3v//+d03/u+++/82f2tUPf/j+pz+1ux++/93vf/oDfmCHH5Zv...
--- id: task_csv_stations_coverage name: Idaho Weather Stations Coverage Gap Analysis category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/idaho_weather_stations.csv dest: idaho_weather_stations.csv --- ## Prompt I ha...
task_csv_stations_filter
Idaho Weather Stations Multi-Criteria Filtering
csv_analysis
hybrid
180
I have a CSV file `idaho_weather_stations.csv` in my workspace containing data on 213 weather stations across Idaho. The file has columns: `OBJECTID`, `Station Name`, `Station Code`, `Managing Agency`, `County`, `Longitude`, `Latitude`, `Elevation (feet)`, `x`, `y`. Please perform the following filtering and analysis ...
The agent should: 1. Parse the CSV file, handling the BOM and DMS-format coordinates 2. Filter NWS stations >= 5,000 ft: find exactly 39 stations, highest being GALENA at 7,300 ft 3. Filter NRCS in Custer County: find 4 stations (DOLLARHIDE SUMMIT SNOTEL 8420, HILTS CREEK SNOTEL 8000, BEAR CANYON SNOTEL 7900, STICKNEY...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the multi-criteria filtering task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to s...
### Criterion 1: Filter Accuracy (Weight: 40%) **Score 1.0**: All four filters produce correct results with exact counts (39, 4, 8) and correct station lists. DMS latitude parsing is handled correctly. **Score 0.75**: Three of four filters are correct; one has minor errors. **Score 0.5**: Two filters are correct; othe...
- [ ] Report file `filter_report.md` is created - [ ] NWS >= 5000 ft count is 39 - [ ] GALENA identified as highest NWS station at 7,300 ft - [ ] NRCS Custer County count is 4 - [ ] DOLLARHIDE SUMMIT SNOTEL identified at 8,420 ft - [ ] DMS latitude correctly parsed for southern filter - [ ] 8 low-elevation southern sta...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/idaho_weather_stations.csv", "dest": "idaho_weather_stations.csv"}]
["csvs/idaho_weather_stations.csv"]
H4sIAAAAAAAC/+1cy44ryXGd9QDzD7WzDZTK+X4sq8nqJtUkq0WyL9XaCANpLAuwZwDPSLa+zQt/kn/B50RmFVlkS94Y9sJqSHf63ts3Kisz4sQ5EZH8zY9//PHvf//bb//xh1//63ff/vSP3/3Lr3/86duffv/D9z92v/nxj1/9T3wpfAXn5L/4uvuvc0Hp6c/Kn2uno/uqUV/9L3z9Aa/7L3j8V/8/v/7z3/9jfPr5sDpv1+2pHHxz+Pafv5t/s/rht9+1+2+///Z3v//+d03/u+++/82f2tUPf/j+pz+1ux++/93vf/oDfmCHH5Zv...
--- id: task_csv_stations_filter name: Idaho Weather Stations Multi-Criteria Filtering category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/idaho_weather_stations.csv dest: idaho_weather_stations.csv --- ## Prompt I h...
task_csv_stock_best_worst
Apple Stock 2014 Best and Worst Days
csv_analysis
hybrid
180
I have a CSV file `apple_stock_2014.csv` in my workspace containing Apple (AAPL) adjusted closing prices for 2014. The file has two columns: `AAPL_x` (date in YYYY-MM-DD format) and `AAPL_y` (adjusted closing price). There are 240 trading days of data. Please find the best and worst trading days for Apple stock in 201...
The agent should: 1. Read and parse the CSV file 2. Calculate daily close-to-close percentage changes for all 239 day pairs 3. Sort to find the best and worst days 4. Check for clustering patterns among extreme days 5. Calculate the positive/negative day split 6. Write a well-structured markdown report Key expected v...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the best/worst trading days analysis task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion na...
### Criterion 1: Analytical Accuracy (Weight: 35%) **Score 1.0**: All top 5 best and worst days are correctly identified with accurate dates, dollar changes, and percentage changes matching expected values. **Score 0.75**: Most days are correctly identified with one or two minor errors in values or ranking. **Score 0....
- [ ] Report file `best_worst_days_report.md` is created - [ ] Top 5 best days correctly identified with dates - [ ] Percentage changes for best days are accurate - [ ] Top 5 worst days correctly identified with dates - [ ] Percentage changes for worst days are accurate - [ ] Dollar changes shown for extreme days - [ ]...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/apple_stock_2014.csv", "dest": "apple_stock_2014.csv"}]
["csvs/apple_stock_2014.csv"]
H4sIAAAAAAAC/+1Yu65kxw28sb7CH3A1bj6aj1C5Av3BQlg4sgELXlmw/95FjqbZcGwo8XRyoRKXh80qPnq+fvvt259//uWXv/3ly7df//71r194kT6+fvvt4393Fo6p9l+c//pLvO1gT5xYyT7+tD7+gPPPb7/+/A98/uP/8/zww08/fvnXZ//593dF/veLvl/86f5Q3ZLqe2ApeOnevlUHtk/XR6Sl87pg//T9iG1qNGAUuJJSzXPg/HR78GIT8jgwrbJmWRTigyIMeYRGku+JjrRdE8nmy3hXzKQapMQDW8Eia4fYZe0VB2LebGQH...
--- id: task_csv_stock_best_worst name: Apple Stock 2014 Best and Worst Days category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/apple_stock_2014.csv dest: apple_stock_2014.csv --- ## Prompt I have a CSV file `apple_...
task_csv_stock_trend
Apple Stock 2014 Trend Analysis
csv_analysis
hybrid
180
I have a CSV file `apple_stock_2014.csv` in my workspace containing Apple (AAPL) adjusted closing prices for 2014. The file has two columns: `AAPL_x` (date in YYYY-MM-DD format) and `AAPL_y` (adjusted closing price). There are 240 trading days of data. Please analyze the overall price trend for Apple stock over 2014 a...
The agent should: 1. Read and parse the CSV file 2. Compute the starting price ($77.45 on 2014-01-02) and ending price ($110.03 on 2014-12-12) 3. Calculate the overall percentage change (~42.07%) 4. Determine the overall trend is bullish (significant upward movement) 5. Compute monthly average prices showing the upwar...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the stock trend analysis task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to score...
### Criterion 1: Data Analysis Accuracy (Weight: 35%) **Score 1.0**: All computed values (starting/ending prices, percentage change, streaks) are numerically correct and clearly presented. **Score 0.75**: Most values are correct with one or two minor numerical discrepancies. **Score 0.5**: Some values are correct but ...
- [ ] Report file `stock_trend_report.md` is created - [ ] Overall trend correctly identified as bullish/upward - [ ] Starting price correctly reported (~$77.45) - [ ] Ending price correctly reported (~$110.03) - [ ] Percentage change correctly calculated (~42%) - [ ] Monthly average prices or monthly breakdown include...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/apple_stock_2014.csv", "dest": "apple_stock_2014.csv"}]
["csvs/apple_stock_2014.csv"]
H4sIAAAAAAAC/+1Yu65kxw28sb7CH3A1bj6aj1C5Av3BQlg4sgELXlmw/95FjqbZcGwo8XRyoRKXh80qPnq+fvvt259//uWXv/3ly7df//71r194kT6+fvvt4393Fo6p9l+c//pLvO1gT5xYyT7+tD7+gPPPb7/+/A98/uP/8/zww08/fvnXZ//593dF/veLvl/86f5Q3ZLqe2ApeOnevlUHtk/XR6Sl87pg//T9iG1qNGAUuJJSzXPg/HR78GIT8jgwrbJmWRTigyIMeYRGku+JjrRdE8nmy3hXzKQapMQDW8Eia4fYZe0VB2LebGQH...
--- id: task_csv_stock_trend name: Apple Stock 2014 Trend Analysis category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/apple_stock_2014.csv dest: apple_stock_2014.csv --- ## Prompt I have a CSV file `apple_stock_2014...
task_csv_stock_volatility
Apple Stock 2014 Volatility Analysis
csv_analysis
hybrid
180
I have a CSV file `apple_stock_2014.csv` in my workspace containing Apple (AAPL) adjusted closing prices for 2014. The file has two columns: `AAPL_x` (date in YYYY-MM-DD format) and `AAPL_y` (adjusted closing price). There are 240 trading days of data. Please analyze the daily volatility for Apple stock in 2014 and wr...
The agent should: 1. Read and parse the CSV file 2. Calculate daily percentage returns as `(price_today - price_yesterday) / price_yesterday * 100` 3. Compute the standard deviation of daily returns (~1.47%) 4. Annualize the volatility (~23.35%) 5. Identify the top 5 most volatile days by absolute daily percentage cha...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the stock volatility analysis task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to ...
### Criterion 1: Analytical Accuracy (Weight: 35%) **Score 1.0**: All volatility metrics are correctly computed: daily std dev (~1.47%), annualized volatility (~23%), top volatile days match expected values, and quarterly breakdowns are accurate. **Score 0.75**: Most metrics are correct with one or two minor numerical...
- [ ] Report file `volatility_report.md` is created - [ ] Daily return standard deviation correctly calculated (~1.47%) - [ ] Annualized volatility correctly calculated (~23%) - [ ] Top 5 most volatile days identified with correct dates - [ ] Daily percentage changes shown for volatile days - [ ] Quarterly volatility b...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/apple_stock_2014.csv", "dest": "apple_stock_2014.csv"}]
["csvs/apple_stock_2014.csv"]
H4sIAAAAAAAC/+1Yu65kxw28sb7CH3A1bj6aj1C5Av3BQlg4sgELXlmw/95FjqbZcGwo8XRyoRKXh80qPnq+fvvt259//uWXv/3ly7df//71r194kT6+fvvt4393Fo6p9l+c//pLvO1gT5xYyT7+tD7+gPPPb7/+/A98/uP/8/zww08/fvnXZ//593dF/veLvl/86f5Q3ZLqe2ApeOnevlUHtk/XR6Sl87pg//T9iG1qNGAUuJJSzXPg/HR78GIT8jgwrbJmWRTigyIMeYRGku+JjrRdE8nmy3hXzKQapMQDW8Eia4fYZe0VB2LebGQH...
--- id: task_csv_stock_volatility name: Apple Stock 2014 Volatility Analysis category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/apple_stock_2014.csv dest: apple_stock_2014.csv --- ## Prompt I have a CSV file `apple_...
task_csv_temp_anomalies
Global Temperature Anomaly Detection
csv_analysis
hybrid
180
I have a CSV file `global_temperature.csv` in my workspace containing global temperature anomaly data. The file has three columns: `Source` (either "GISTEMP" or "gcag"), `Year` (in YYYY-MM format), and `Mean` (temperature anomaly in °C relative to a baseline period). GISTEMP data covers 1880–2023 and gcag data covers ...
The agent should: 1. Read and parse the CSV file 2. Filter for GISTEMP records (1880–2023) 3. Identify the hottest month: September 2023 at +1.48°C 4. Identify the coldest month: January 1893 at -0.82°C 5. Calculate per-calendar-month statistics and find z-score outliers 6. Compute annual averages and rank years 7. Ca...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the temperature anomaly detection task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names...
### Criterion 1: Data Analysis Accuracy (Weight: 35%) **Score 1.0**: Extreme months, warmest/coldest years, z-scores, and year-over-year changes are all numerically correct and clearly presented with specific values. **Score 0.75**: Most values are correct with one or two minor discrepancies. **Score 0.5**: Some value...
- [ ] Report file `anomaly_report.md` is created - [ ] Hottest month correctly identified as 2023-09 with value ~1.48°C - [ ] Coldest month correctly identified as 1893-01 with value ~-0.82°C - [ ] Top warmest years include 2023, 2016, and 2020 - [ ] Top coldest years include 1909 and 1904 - [ ] Statistical outlier ana...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/global_temperature.csv", "dest": "global_temperature.csv"}]
["csvs/global_temperature.csv"]
H4sIAAAAAAAC/+19za5uO25cjwPkHfwA145E/T+AEWRgIIAzySi4aVz0xImD291+/nzfEkmpiqtnQSbee3KA2nW0tfRDUSWK+uOf/+3P/+lP//Kv//PXf/kff/ntf/2f337/9S9//f23f/jjn//tD/+vftLnp9f6/Pv5wX+lliH+u43nUkr+w9+lP/x/+Pnrn//y6++fP/+Hf58///yvf/39j7/98t9/+/X3X/7pt1//93/8D3/6469/+iXPlv4+5V/+Pv1DH7UDKl/000MV0PJF28oF0Pqgcw5A24OmOQHtT7m1CqDji+a2kDu/qKSB...
--- id: task_csv_temp_anomalies name: Global Temperature Anomaly Detection category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/global_temperature.csv dest: global_temperature.csv --- ## Prompt I have a CSV file `glob...
task_csv_temp_decades
Global Temperature Decade Comparison
csv_analysis
hybrid
180
I have a CSV file `global_temperature.csv` in my workspace containing global temperature anomaly data. The file has three columns: `Source` (either "GISTEMP" or "gcag"), `Year` (in YYYY-MM format), and `Mean` (temperature anomaly in °C relative to a baseline period). GISTEMP data covers 1880–2023 and gcag data covers ...
The agent should: 1. Read and parse the CSV, separating GISTEMP and gcag records 2. Compute annual averages from monthly data for each source 3. Group annual averages by decade and compute statistics 4. Calculate decade-to-decade transitions 5. Compare the two data sources for their overlapping period 6. Write a struc...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the decade comparison task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to scores (...
### Criterion 1: Quantitative Accuracy (Weight: 35%) **Score 1.0**: Decade averages, transitions, and variability measures are numerically correct and clearly presented. Values match expected ranges. **Score 0.75**: Most values correct with minor discrepancies in one or two decades. **Score 0.5**: Some values correct ...
- [ ] Report file `decade_report.md` is created - [ ] Decade average table included with values for 1880s through 2010s - [ ] 1910s or 1900s correctly identified as the coldest full decade - [ ] 2010s correctly identified as the warmest full decade - [ ] Decade-to-decade changes computed - [ ] Largest warming transitio...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/global_temperature.csv", "dest": "global_temperature.csv"}]
["csvs/global_temperature.csv"]
H4sIAAAAAAAC/+19za5uO25cjwPkHfwA145E/T+AEWRgIIAzySi4aVz0xImD291+/nzfEkmpiqtnQSbee3KA2nW0tfRDUSWK+uOf/+3P/+lP//Kv//PXf/kff/ntf/2f337/9S9//f23f/jjn//tD/+vftLnp9f6/Pv5wX+lliH+u43nUkr+w9+lP/x/+Pnrn//y6++fP/+Hf58///yvf/39j7/98t9/+/X3X/7pt1//93/8D3/6469/+iXPlv4+5V/+Pv1DH7UDKl/000MV0PJF28oF0Pqgcw5A24OmOQHtT7m1CqDji+a2kDu/qKSB...
--- id: task_csv_temp_decades name: Global Temperature Decade Comparison category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/global_temperature.csv dest: global_temperature.csv --- ## Prompt I have a CSV file `global...
task_csv_temp_trend
Global Temperature Trend Analysis
csv_analysis
hybrid
180
I have a CSV file `global_temperature.csv` in my workspace containing global temperature anomaly data. The file has three columns: `Source` (either "GISTEMP" or "gcag"), `Year` (in YYYY-MM format), and `Mean` (temperature anomaly in °C relative to a baseline period). GISTEMP data covers 1880–2023 and gcag data covers ...
The agent should: 1. Read and parse the CSV, filtering for GISTEMP 2. Compute annual averages from monthly data (using only years with all 12 months) 3. Fit linear regressions to determine warming rates 4. Compare pre-1950 and post-1950 trends to show acceleration 5. Find coldest and warmest 10-year stretches 6. Ident...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the temperature trend analysis task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to...
### Criterion 1: Quantitative Accuracy (Weight: 35%) **Score 1.0**: Linear regression slopes, decade rates, and milestone years are all numerically correct and clearly stated with units. **Score 0.75**: Most values correct with one or two minor errors in less critical figures. **Score 0.5**: Some values correct but ke...
- [ ] Report file `trend_report.md` is created - [ ] Overall warming rate approximately 0.07–0.09°C per decade reported - [ ] Pre-1950 warming rate computed (~0.03–0.05°C/decade) - [ ] Post-1950 warming rate computed (~0.14–0.17°C/decade) - [ ] Acceleration noted (post-1950 rate significantly faster than pre-1950) - [ ...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "csvs/global_temperature.csv", "dest": "global_temperature.csv"}]
["csvs/global_temperature.csv"]
H4sIAAAAAAAC/+19za5uO25cjwPkHfwA145E/T+AEWRgIIAzySi4aVz0xImD291+/nzfEkmpiqtnQSbee3KA2nW0tfRDUSWK+uOf/+3P/+lP//Kv//PXf/kff/ntf/2f337/9S9//f23f/jjn//tD/+vftLnp9f6/Pv5wX+lliH+u43nUkr+w9+lP/x/+Pnrn//y6++fP/+Hf58///yvf/39j7/98t9/+/X3X/7pt1//93/8D3/6469/+iXPlv4+5V/+Pv1DH7UDKl/000MV0PJF28oF0Pqgcw5A24OmOQHtT7m1CqDji+a2kDu/qKSB...
--- id: task_csv_temp_trend name: Global Temperature Trend Analysis category: csv_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: csvs/global_temperature.csv dest: global_temperature.csv --- ## Prompt I have a CSV file `global_temp...
task_cve_security_triage
CVE/Security Triage
analysis
hybrid
300
You are a security engineer triaging the results of a dependency vulnerability scan for your company's production web application. The scan results are in `vulnerability_scan.json` and the deployment context is in `deployment_context.md`. Review all 10 CVEs in the scan report and produce two deliverables: 1. **`triag...
The agent should: 1. Read and parse `vulnerability_scan.json` to understand all 10 CVEs 2. Read `deployment_context.md` to understand the deployment context, change management policy, and security posture 3. Apply contextual reasoning to map CVSS severity to operational priority: - CVE-2026-29112 (express RCE, CVSS...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the CVE/Security Triage task. Checks for structural completeness of both deliverables and correctness of key priority assignments. """ from pathlib import Path import re scores = {} workspace = Path(works...
### Criterion 1: Priority Assignment Accuracy (Weight: 30%) **Score 1.0**: All priorities are well-justified and contextually appropriate. Both CRITICAL CVEs (express RCE and JWT bypass) are P0 — especially the JWT bypass given active exploitation. The pg SQL injection is P0 or P1 with clear reasoning about PCI scope....
- [ ] `triage_report.md` is created - [ ] `remediation_plan.md` is created - [ ] All 10 CVEs appear in the triage report - [ ] Each CVE has a priority assigned (P0-P3) - [ ] Express RCE (CVE-2026-29112) is classified as P0 - [ ] JWT bypass (CVE-2026-31845) is classified as P0 - [ ] Build-only CVEs (minimatch, semver) a...
{"automated": 0.4, "llm_judge": 0.6}
false
[]
[]
[{"path": "vulnerability_scan.json", "content": "{\n \"scan_metadata\": {\n \"scanner\": \"AcmeSec Vulnerability Scanner v3.2.1\",\n \"scan_date\": \"2026-04-10T06:00:00Z\",\n \"target\": \"acme-webapp\",\n \"environment\": \"production\",\n \"total_dependencies\": 187\n },\n \"vulnerabilities\": [\n ...
[]
--- id: task_cve_security_triage name: CVE/Security Triage category: analysis grading_type: hybrid timeout_seconds: 300 grading_weights: automated: 0.4 llm_judge: 0.6 workspace_files: - path: "vulnerability_scan.json" content: | { "scan_metadata": { "scanner": "AcmeSec Vulnerability Sc...
task_daily_summary
Daily Research Summary Generation
productivity
llm_judge
300
You are an executive assistant preparing the daily briefing. Review all files in the research/ folder and write a comprehensive daily summary to daily_briefing.md. The summary should be concise, highlight the most important items requiring executive attention, and be organized with clear sections.
The agent should: 1. Discover and read all files in the `research/` directory 2. Analyze and synthesize information from multiple sources: - Market analysis data - Competitor intelligence - Customer feedback - Product updates - Industry news 3. Create a well-organized daily briefing that: - Opens wit...
### Criterion 1: Information Coverage and Accuracy (Weight: 30%) **Score 1.0**: Summary accurately captures key information from ALL five source documents. Includes market movements, competitor threats (especially Nexus launch and SwiftCloud opportunity), customer risks (MegaCorp churn risk), product milestones (shipp...
- [ ] Agent discovered files in research/ directory - [ ] Agent read all 5 research files - [ ] File `daily_briefing.md` created - [ ] Summary includes executive summary section - [ ] Summary covers market/financial information - [ ] Summary covers competitor intelligence - [ ] Summary covers customer feedback/risks - ...
{}
false
[]
[]
[{"path": "research/market_analysis.txt", "content": "Market Analysis Report - February 15, 2026\nAnalyst: Sarah Chen, Senior Market Strategist\n\nKey Market Movements:\n- S&P 500 closed at 5,842.31, up 1.2% on strong tech earnings\n- NASDAQ gained 1.8%, led by semiconductor stocks\n- Dow Jones rose 0.7% to 42,156.88\n...
[]
--- id: task_daily_summary name: Daily Research Summary Generation category: productivity grading_type: llm_judge timeout_seconds: 300 workspace_files: - path: "research/market_analysis.txt" content: | Market Analysis Report - February 15, 2026 Analyst: Sarah Chen, Senior Market Strategist Key ...
task_deep_research
Deep Research with Citations
research
llm_judge
300
Research the following topic and produce a comprehensive report with primary source citations: **Topic: The current state of WebAssembly (Wasm) adoption outside the browser — specifically in server-side, edge computing, and plugin systems.** Your report should cover: 1. **Current adoption**: Which major platforms an...
The agent should: 1. Use web search to find current information about WebAssembly outside the browser 2. Visit primary sources (official project sites, blog posts, documentation) 3. Synthesize findings into a well-structured research report 4. Include inline citations and a references section 5. Cover all five request...
### Criterion 1: Research Depth and Accuracy (Weight: 30%) **Score 1.0**: Report contains specific, verifiable information from primary sources. Demonstrates genuine research — mentions specific version numbers, dates, project names, and details that could only come from actual investigation. No significant inaccuraci...
- [ ] File `wasm_research.md` created - [ ] Report covers WebAssembly adoption outside the browser - [ ] Multiple Wasm runtimes are compared - [ ] WASI is discussed with specific proposal details - [ ] At least 5 concrete use cases with named companies/projects - [ ] Citations are present (URLs or specific source refer...
{}
false
[]
[]
[]
[]
--- id: task_deep_research name: Deep Research with Citations category: research grading_type: llm_judge timeout_seconds: 300 workspace_files: [] --- ## Prompt Research the following topic and produce a comprehensive report with primary source citations: **Topic: The current state of WebAssembly (Wasm) adoption outs...
task_dockerfile_optimization
Dockerfile Optimization
coding
automated
120
The file `Dockerfile` in your workspace builds a Python web application but is bloated and inefficient. Optimize it to produce a smaller, faster, and more production-ready Docker image. Save the optimized Dockerfile as `Dockerfile.optimized`. Goals: 1. Reduce the number of layers by consolidating `RUN` instructions ...
The agent should analyze the original Dockerfile and produce `Dockerfile.optimized` that applies common Docker best practices: - Switch to a slimmer base image such as `python:3.11-slim`, `python:3.12-slim`, or an Alpine variant. - Merge the many individual `apt-get install` or `apk add` commands into a single `RUN` l...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import re scores = { "file_created": 0.0, "smaller_base_image": 0.0, "consolidated_run": 0.0, "removed_editors": 0.0, "requirements_before_source": 0.0, "cache_cleanu...
- [ ] File `Dockerfile.optimized` is created - [ ] Uses a smaller base image than `ubuntu:22.04` - [ ] Consolidates RUN instructions (fewer layers) - [ ] Removes unnecessary editor/tool packages (vim, nano) - [ ] Copies requirements.txt before full source for layer caching - [ ] Cleans package manager cache in the inst...
{}
false
[]
[]
[{"path": "Dockerfile", "content": "FROM ubuntu:22.04\n\nRUN apt-get update\nRUN apt-get install -y python3\nRUN apt-get install -y python3-pip\nRUN apt-get install -y curl\nRUN apt-get install -y wget\nRUN apt-get install -y git\nRUN apt-get install -y vim\nRUN apt-get install -y nano\nRUN apt-get install -y build-ess...
[]
--- id: task_dockerfile_optimization name: Dockerfile Optimization category: coding grading_type: automated timeout_seconds: 120 workspace_files: - path: "Dockerfile" content: | FROM ubuntu:22.04 RUN apt-get update RUN apt-get install -y python3 RUN apt-get install -y python3-pip RU...
task_earnings_analysis
Earnings Analysis
analysis
automated
240
How many basis points did GitLab beat or miss Q3 2025 margin guidance? Research the underlying earnings release or transcript, determine the relevant guided margin and the actual reported margin, and save your answer to `gitlab_margin_guidance.txt`. Include the actual margin, the guided margin, and the basis point be...
The agent should: 1. Identify the GitLab quarter in question and the relevant margin metric from management commentary. 2. Find the guidance level and the actual reported result. 3. Convert the difference into basis points. 4. Save the answer to `gitlab_margin_guidance.txt`. GitLab's Q3 FY2026 commentary states that ...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import re workspace = Path(workspace_path) answer_file = workspace / "gitlab_margin_guidance.txt" if not answer_file.exists(): return { "file_created": 0.0, "mentions_metric...
- [ ] File `gitlab_margin_guidance.txt` created - [ ] File identifies GitLab and the margin metric - [ ] File includes actual and guided margin values - [ ] File concludes GitLab beat guidance by about 500 basis points - [ ] Explanation is concise and readable
{}
false
[]
[]
[]
[]
--- id: task_earnings_analysis name: Earnings Analysis category: analysis grading_type: automated timeout_seconds: 240 workspace_files: [] --- ## Prompt How many basis points did GitLab beat or miss Q3 2025 margin guidance? Research the underlying earnings release or transcript, determine the relevant guided margin ...
task_eli5_pdf_summary
ELI5 PDF Summarization
analysis
llm_judge
300
Read the file `GPT4.pdf` in my workspace. It's a technical paper about GPT-4. Write an "Explain Like I'm 5" (ELI5) summary of the paper and save it to `eli5_summary.txt`. The summary should: - Be understandable by a young child with no technical background - Use simple words, short sentences, and everyday analogies - ...
The agent should: 1. Read and parse the PDF file `GPT4.pdf` (the GPT-4 Technical Report by OpenAI) 2. Identify the key themes and findings of the paper, including: - GPT-4 is a large multimodal model that accepts text and image inputs and produces text outputs - It demonstrates human-level performance on various...
### Criterion 1: Simplicity and Accessibility (Weight: 35%) **Score 1.0**: The summary is genuinely understandable by a young child. Sentences are short and simple. No technical jargon whatsoever. Uses concrete, everyday language and relatable analogies (e.g., comparing the model to a helpful friend, a very good stude...
- [ ] Agent successfully reads/parses the PDF file - [ ] Output file `eli5_summary.txt` is created - [ ] Summary explains what GPT-4 is in simple terms - [ ] Summary covers what GPT-4 can do (understanding text, images, answering questions, exams) - [ ] Summary mentions that people worked to make it safer/nicer - [ ] S...
{}
false
[]
[]
[{"source": "GPT4.pdf", "dest": "GPT4.pdf"}]
["GPT4.pdf"]
H4sIAAAAAAAC/+SaY7AwzZKgj22f8x7btm3btm3btvEe27bt99i2rf327mzszNz5Md+P3bgRmxHdlZFdXf10RFVmdmWLySsz09obmwL8XxT6v4SVmfkf7V/y71smZno2JnoW1v9t+zc7G+NfDT49wP8DcXFyNnD865EA/38KibywKA0DLQsUSRwUGz49vp2hJRQ3Nz6dsoe9CT6dupyhpYmRMz6dkouh8z8sonaONvh0goJ27vhaf3Wnx2eg/1+HDhSdqIW1s4njX32sDZxNhE2M7Iz/7YZ/DMaATydtYmvmbI7PwAJFJ2Pg7GjxP8dg...
--- id: task_eli5_pdf_summary name: ELI5 PDF Summarization category: analysis grading_type: llm_judge timeout_seconds: 300 workspace_files: - source: GPT4.pdf dest: GPT4.pdf --- ## Prompt Read the file `GPT4.pdf` in my workspace. It's a technical paper about GPT-4. Write an "Explain Like I'm 5" (ELI5) summary o...
task_email
Professional Email Drafting
writing
llm_judge
180
Write a professional email declining a meeting request due to schedule conflicts. Save it to email_draft.txt.
The agent should: 1. Create a professional email with appropriate structure 2. Include key elements: greeting, explanation, decline, alternative/apology, closing 3. Maintain a polite and professional tone 4. Be concise but complete 5. Save to a file named `email_draft.txt` The email should balance being direct about ...
### Criterion 1: Professional Tone and Courtesy (Weight: 30%) **Score 1.0**: Tone is consistently professional, polite, and courteous. Expresses appropriate regret about declining. Maintains positive relationship focus. No overly casual or stiff language. **Score 0.75**: Generally professional and polite with minor t...
- [ ] File `email_draft.txt` created - [ ] Email has proper structure (greeting, body, closing) - [ ] Clearly declines the meeting request - [ ] Provides reason (schedule conflict) - [ ] Maintains professional and polite tone - [ ] Offers alternative or expresses willingness to reschedule - [ ] Includes appropriate clo...
{}
false
[]
[]
[]
[]
--- id: task_email name: Professional Email Drafting category: writing grading_type: llm_judge timeout_seconds: 180 workspace_files: [] --- ## Prompt Write a professional email declining a meeting request due to schedule conflicts. Save it to email_draft.txt. ## Expected Behavior The agent should: 1. Create a prof...
task_email_reply_drafting
Email Reply Drafting from Unread Inbox
writing
llm_judge
240
You have 5 unread emails in the `inbox/` folder (`unread_01` through `unread_05`). Read all unread messages and draft replies for the emails that require a response. Save your output to `reply_drafts.md`. Requirements: 1. Only draft replies for emails that need a reply (do not draft replies for low-value newsletters...
The agent should inspect all 5 emails and determine which require action. It should skip the newsletter and draft replies for the operationally relevant messages: - vendor security questionnaire reminder - customer escalation about failed export - internal review request - partner meeting reschedule Strong responses ...
### Criterion 1: Coverage and Filtering (Weight: 25%) **Score 1.0**: Reviews all 5 emails, drafts replies for 01/02/03/05, and correctly omits drafting a reply to the newsletter (04). **Score 0.75**: Correctly drafts most required replies with one minor filtering mistake (e.g., includes newsletter or misses one requi...
- [ ] File `reply_drafts.md` created - [ ] All 5 unread emails were reviewed - [ ] Reply drafts are included for emails that require a response (01, 02, 03, 05) - [ ] No unnecessary draft is written for newsletter email (04) - [ ] Each draft includes source file and `Re:` subject - [ ] Tone matches context and urgency ...
{}
false
[]
[]
[{"path": "inbox/unread_01_vendor_security_followup.txt", "content": "From: rachel.owens@vendorco.com (Rachel Owens, VendorCo Security)\nTo: me@mycompany.com\nDate: Tue, 07 Apr 2026 08:12:00 -0500\nSubject: Follow-up: Security questionnaire due tomorrow\n\nHi,\n\nQuick reminder that we still need your completed securit...
[]
--- id: task_email_reply_drafting name: Email Reply Drafting from Unread Inbox category: writing grading_type: llm_judge timeout_seconds: 240 workspace_files: - path: "inbox/unread_01_vendor_security_followup.txt" content: | From: rachel.owens@vendorco.com (Rachel Owens, VendorCo Security) To: me@myco...
task_email_search
Email Search and Summarization
analysis
hybrid
240
You have access to a collection of emails in the `emails/` folder in your workspace (11 email files with dates in their filenames). Search through all the emails to find everything related to "Project Alpha" and create a comprehensive summary document. Save the summary to alpha_summary.md with the following sections: ...
The agent should: 1. Discover and read all email files in the `emails/` directory 2. Identify which emails are related to Project Alpha (9 of 11 are relevant; 2 are unrelated noise) 3. Filter out unrelated emails (team lunch, conference promo) 4. Synthesize information across multiple emails into a coherent narrative ...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the email search and summarization task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion name...
### Criterion 1: Information Completeness (Weight: 25%) **Score 1.0**: Summary captures all major aspects of Project Alpha from all 10 relevant emails: project definition, tech stack, budget (original and revised), timeline (original and revised), data pipeline architecture, API design, security findings, Phase 1 comp...
- [ ] Agent discovered and read emails from the emails/ directory - [ ] File `alpha_summary.md` created - [ ] Summary correctly identifies Project Alpha as an analytics dashboard - [ ] Technology stack mentioned (PostgreSQL/TimescaleDB, FastAPI, React, Kafka, etc.) - [ ] Original budget ($340K) and revised budget ($410...
{"automated": 0.4, "llm_judge": 0.6}
false
[]
[]
[{"path": "emails/2026-01-15_project_alpha_kickoff.txt", "content": "From: sarah.chen@mycompany.com (Sarah Chen, Project Lead)\nTo: engineering-team@mycompany.com\nDate: Thu, 15 Jan 2026 09:00:00 -0500\nSubject: Project Alpha - Kickoff and Timeline\n\nHi team,\n\nExcited to announce that Project Alpha has been official...
[]
--- id: task_email_search name: Email Search and Summarization category: analysis grading_type: hybrid timeout_seconds: 240 grading_weights: automated: 0.4 llm_judge: 0.6 workspace_files: - path: "emails/2026-01-15_project_alpha_kickoff.txt" content: | From: sarah.chen@mycompany.com (Sarah Chen, Project...
task_email_triage
Email Inbox Triage
productivity
hybrid
240
You are helping triage an overflowing email inbox. The emails have been provided to you in the `inbox/` folder in your workspace (files named `email_01.txt` through `email_13.txt`). Read all 13 emails and create a triage report saved to `triage_report.md`. For each email, assign: 1. **Priority**: P0 (drop everything),...
The agent should: 1. Discover and read all 13 email files in the `inbox/` directory 2. Analyze each email for urgency, sender importance, deadlines, and content 3. Assign appropriate priority levels considering: - Production incidents are P0 - High-value client communications are P1 - Security compliance dead...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the email triage task based on structural correctness and key priority assignments. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory R...
### Criterion 1: Priority Assignment Accuracy (Weight: 30%) **Score 1.0**: All priorities are correctly assigned. Production outage (email 01) and correlated monitoring alert (email 13) are P0. BigClient follow-up (email 05) is P0 or P1. Security password rotation (email 08) is P1 or P2 given its 2-day deadline. Auth ...
- [ ] Agent discovered and read all 13 emails in inbox/ - [ ] File `triage_report.md` created - [ ] All 13 emails are present in the report - [ ] Each email has a priority assigned (P0-P4) - [ ] Each email has a category assigned - [ ] Each email has a recommended action - [ ] Production outage email (01) is classified...
{"automated": 0.4, "llm_judge": 0.6}
false
[]
[]
[{"path": "inbox/email_01.txt", "content": "From: cto@mycompany.com (David Park, CTO)\nTo: me@mycompany.com\nDate: Mon, 17 Feb 2026 08:02:00 -0500\nSubject: URGENT: Production database outage - all hands needed\n\nOur primary production database cluster went down at 7:45am EST. Customer-facing\nservices are returning 5...
[]
--- id: task_email_triage name: Email Inbox Triage category: productivity grading_type: hybrid timeout_seconds: 240 grading_weights: automated: 0.4 llm_judge: 0.6 workspace_files: - path: "inbox/email_01.txt" content: | From: cto@mycompany.com (David Park, CTO) To: me@mycompany.com Date: Mon...
task_eu_regulation_research
EU AI Act Compliance Research
research
llm_judge
300
A SaaS company building AI-powered developer tools needs to understand their obligations under the **EU AI Act (Regulation 2024/1689)**. They sell to European customers and deploy models via API. Research the EU AI Act and create a compliance briefing. Your report should cover: 1. **Risk classification**: How does th...
The agent should: 1. Research the EU AI Act using web search and/or official EU sources 2. Find the specific regulation text and key provisions 3. Accurately classify risk levels and associated obligations 4. Identify GPAI-specific provisions (relevant for companies using LLMs) 5. Create an actionable compliance brief...
### Criterion 1: Legal Accuracy (Weight: 30%) **Score 1.0**: Risk classification is accurately described with correct tier definitions. Article numbers are cited correctly. GPAI provisions (Articles 51-56) are accurately summarized. Penalty amounts match the regulation (up to €35M or 7% of turnover for prohibited prac...
- [ ] File `eu_ai_act_briefing.md` created - [ ] Risk classification system accurately described - [ ] Four risk tiers identified (prohibited, high, limited, minimal) - [ ] GPAI/foundation model provisions discussed - [ ] Compliance timeline with dates - [ ] Penalty amounts mentioned - [ ] Transparency requirements des...
{}
false
[]
[]
[]
[]
--- id: task_eu_regulation_research name: EU AI Act Compliance Research category: research grading_type: llm_judge timeout_seconds: 300 workspace_files: [] --- ## Prompt A SaaS company building AI-powered developer tools needs to understand their obligations under the **EU AI Act (Regulation 2024/1689)**. They sell t...
task_events
Tech Conference Research
research
llm_judge
300
Find 5 upcoming tech conferences and create events.md with name, date, location, and website for each.
The agent should: 1. Use web search or research tools to find legitimate tech conferences 2. Identify 5 conferences happening this year 3. Extract key information: name, date, location, website 4. Create a file named `events.md` with this information 5. Format the information clearly and consistently 6. Ensure the con...
### Criterion 1: Information Accuracy (Weight: 35%) **Score 1.0**: All 5 conferences are real, verifiable tech conferences. All information (names, dates, locations, websites) is accurate and can be verified. No fabricated events. **Score 0.75**: 4-5 conferences are verifiable with accurate information. Minor inaccur...
- [ ] File `events.md` created - [ ] Contains exactly 5 conference entries - [ ] Each entry has conference name - [ ] Each entry has date information - [ ] Each entry has location information - [ ] Each entry has website/URL - [ ] Information appears accurate and verifiable - [ ] Formatting is clear and consistent
{}
false
[]
[]
[]
[]
--- id: task_events name: Tech Conference Research category: research grading_type: llm_judge timeout_seconds: 300 workspace_files: [] --- ## Prompt Find 5 upcoming tech conferences and create events.md with name, date, location, and website for each. ## Expected Behavior The agent should: 1. Use web search or res...
task_executive_lookup
Executive Lookup
research
automated
180
Who is the CFO of GitLab as of April 7, 2026? Research the answer and save it to `gitlab_cfo.txt`. Include the executive's full name and a short source note or date reference showing why the answer is current as of April 7, 2026.
The agent should: 1. Identify GitLab's chief financial officer as of April 7, 2026. 2. Use a reliable source such as GitLab investor relations, a press release, or reputable financial reporting. 3. Create `gitlab_cfo.txt` in the workspace. 4. Include the CFO's full name and a brief date/source note. The answer should...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import re workspace = Path(workspace_path) answer_file = workspace / "gitlab_cfo.txt" if not answer_file.exists(): return { "file_created": 0.0, "correct_name": 0.0, ...
- [ ] File `gitlab_cfo.txt` created - [ ] File identifies Jessica Ross as GitLab CFO - [ ] File references GitLab and CFO role - [ ] File includes a 2026 date or source note - [ ] Answer is concise and readable
{}
false
[]
[]
[]
[]
--- id: task_executive_lookup name: Executive Lookup category: research grading_type: automated timeout_seconds: 180 workspace_files: [] --- ## Prompt Who is the CFO of GitLab as of April 7, 2026? Research the answer and save it to `gitlab_cfo.txt`. Include the executive's full name and a short source note or date r...
task_files
File Structure Creation
skills
automated
120
Create a project structure with: src/ directory, src/main.py with hello world, README.md with project title, and .gitignore ignoring **pycache**.
The agent should: 1. Create a directory named `src/` 2. Create a file `src/main.py` with a hello world program 3. Create a file `README.md` with a project title 4. Create a file `.gitignore` that includes `__pycache__` 5. Ensure all files have appropriate content This tests the agent's ability to perform basic file o...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the file operations task based on correct file structure creation. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: D...
- [ ] Directory `src/` created - [ ] File `src/main.py` created - [ ] `src/main.py` contains valid Python hello world code - [ ] File `README.md` created - [ ] `README.md` contains a project title/heading - [ ] File `.gitignore` created - [ ] `.gitignore` contains `__pycache__` entry
{}
false
[]
[]
[]
[]
--- id: task_files name: File Structure Creation category: skills grading_type: automated timeout_seconds: 120 workspace_files: [] --- ## Prompt Create a project structure with: src/ directory, src/main.py with hello world, README.md with project title, and .gitignore ignoring **pycache**. ## Expected Behavior The ...
task_financial_ratio_calculation
Financial Ratio Calculation
analysis
automated
240
Calculate the inventory turnover ratio for U.S. Steel for fiscal year 2024. Research the necessary financial figures, perform the calculation, and save the result to `us_steel_inventory_turnover.txt`. Include the formula, the values you used, and the final ratio.
The agent should: 1. Find the relevant FY2024 figures for U.S. Steel needed to compute inventory turnover ratio. 2. Use a standard inventory turnover formula such as cost of sales divided by average inventory. 3. Show the values used in the calculation. 4. Create `us_steel_inventory_turnover.txt` with the final answer...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import re workspace = Path(workspace_path) answer_file = workspace / "us_steel_inventory_turnover.txt" if not answer_file.exists(): return { "file_created": 0.0, "has_inputs...
- [ ] File `us_steel_inventory_turnover.txt` created - [ ] File includes cost of sales and inventory inputs - [ ] File shows a formula or calculation method - [ ] Final ratio is approximately 6.55x - [ ] Answer is readable and financially coherent
{}
false
[]
[]
[]
[]
--- id: task_financial_ratio_calculation name: Financial Ratio Calculation category: analysis grading_type: automated timeout_seconds: 240 workspace_files: [] --- ## Prompt Calculate the inventory turnover ratio for U.S. Steel for fiscal year 2024. Research the necessary financial figures, perform the calculation, a...
task_gh_issue_triage
GitHub Issue Triage
skills
hybrid
300
You have access to a GitHub repository through the `gh` CLI tool (`gh --help` for usage). The repository is `testuser/my-project`. Review the open issues and pull requests: 1. List all open issues and PRs 2. Read each one to understand its content 3. For the most critical issue, add a comment with your analysis and s...
The agent should: 1. Use gh CLI to list and read issues and PRs 2. Analyze each item for urgency and impact 3. Comment on the most critical issue 4. Write a structured triage report to `triage_report.md` This tests the agent's ability to use the gh CLI for a multi-step GitHub workflow: list, read, comment, and synthe...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the GitHub issue triage task. """ from pathlib import Path import re scores = {} workspace = Path(workspace_path) listed_issues = False viewed_pr = False read_detail = False commented = False ...
### Criterion 1: gh CLI Usage (Weight: 30%) **Score 1.0**: Agent fluently used gh CLI to list, view, and comment on issues/PRs with correct parameters. **Score 0.75**: Agent used gh CLI correctly but with minor parameter issues. **Score 0.5**: Agent used some gh commands but struggled with syntax. **Score 0.25**: Agen...
- [ ] Agent listed issues using gh - [ ] Agent listed or viewed PRs using gh - [ ] Agent read individual issues/PRs to understand content - [ ] Agent commented on the most critical issue - [ ] File `triage_report.md` created in workspace - [ ] All open items are present in the report - [ ] Each item has a priority assi...
{"automated": 0.5, "llm_judge": 0.5}
false
[]
["npm:@juppytt/fws", "cli:gh"]
[]
[]
--- id: task_gh_issue_triage name: GitHub Issue Triage category: skills grading_type: hybrid timeout_seconds: 300 grading_weights: automated: 0.5 llm_judge: 0.5 workspace_files: [] prerequisites: - npm:@juppytt/fws - cli:gh --- ## Prompt You have access to a GitHub repository through the `gh` CLI tool (`gh --...
task_git_rescue_recovery
Git Rescue / Recovery
coding
automated
120
Translate this git recovery request into commands and save them to `recovery.sh`, one command per line, with no explanation: I accidentally made my last 2 commits on `main`, but they belong on a new branch named `feature/login-fix`. The commits have not been pushed. Move those 2 commits to the new branch and leave `ma...
The agent should write a sequence of git commands to `recovery.sh` that correctly repairs the repository state. Successful solutions must: 1. Preserve the last two commits on a new branch named `feature/login-fix` 2. Move `main` back by two commits so it points to the prior base commit 3. Leave the repository in a cl...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import subprocess import tempfile scores = { "file_created": 0.0, "git_only_commands": 0.0, "executes_successfully": 0.0, "feature_branch_created": 0.0, "main_reset_corre...
- [ ] File `recovery.sh` is created - [ ] File contains non-empty git commands only - [ ] Commands execute successfully in the controlled repo fixture - [ ] Branch `feature/login-fix` exists after execution - [ ] `main` is reset to the commit before the last two commits - [ ] The two misplaced commits are preserved on ...
{}
false
[]
[]
[]
[]
--- id: task_git_rescue_recovery name: Git Rescue / Recovery category: coding grading_type: automated timeout_seconds: 120 workspace_files: [] --- # Git Rescue / Recovery ## Prompt Translate this git recovery request into commands and save them to `recovery.sh`, one command per line, with no explanation: I accident...
task_gws_cross_service
GWS Cross-Service Workflow
integrations
hybrid
300
You have access to a Google Workspace account through the `gws` CLI tool (`gws --help` for usage). You received an email from alice@company.com about a "Q3 Planning Meeting". Do the following: 1. Find and read that email to get the meeting details 2. Create a calendar event for the meeting based on what you find in t...
The agent should: 1. Search or list Gmail messages to find the Q3 Planning email from Alice 2. Read the email content for meeting details 3. Use gws Calendar to create an event with the correct summary, time, and attendees 4. Use gws Drive to find the agenda document and add a permission for bob@company.com 5. Write a...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the GWS cross-service workflow task. """ from pathlib import Path scores = {} workspace = Path(workspace_path) read_email = False created_event = False found_file = False shared_file = False def ...
### Criterion 1: Cross-Service Coordination (Weight: 50%) **Score 1.0**: Agent seamlessly navigated Gmail, Calendar, and Drive, extracting information from one service and using it in another. The workflow was logical and efficient. **Score 0.75**: Agent used all three services but with minor inefficiencies or missed ...
- [ ] Agent found and read the Q3 Planning email - [ ] Calendar event created with relevant summary - [ ] Calendar event has a start time - [ ] Q3 Planning Agenda document found in Drive - [ ] Permission created on the document for bob@company.com - [ ] File `actions.md` created with a summary of actions taken
{"automated": 0.6, "llm_judge": 0.4}
false
[]
["npm:@juppytt/fws", "cli:gws"]
[]
[]
--- id: task_gws_cross_service name: GWS Cross-Service Workflow category: integrations grading_type: hybrid timeout_seconds: 300 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: [] prerequisites: - npm:@juppytt/fws - cli:gws --- ## Prompt You have access to a Google Workspace account through th...
task_gws_email_triage
GWS Email Triage
integrations
hybrid
300
You have access to a Google Workspace account through the `gws` CLI tool (`gws --help` for usage). Your inbox has several unread emails. Triage them: 1. Check your unread emails 2. Read each message to understand its content and urgency 3. For the most urgent email, draft a reply (save as draft, don't send) 4. Create...
The agent should: 1. Discover and use gws Gmail commands to list and read emails 2. Analyze message content, sender, and urgency 3. Identify the most urgent email and create a draft reply 4. Write a structured triage report to `triage_report.md` This tests the agent's ability to discover and use the gws CLI for a mul...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the GWS email triage task. """ from pathlib import Path import re scores = {} workspace = Path(workspace_path) used_list = False used_get = False used_draft = False def extract_commands(transcrip...
### Criterion 1: GWS CLI Discovery and Usage (Weight: 30%) **Score 1.0**: Agent discovered and fluently used gws Gmail commands (list, get, drafts) with correct parameters. **Score 0.75**: Agent used the gws CLI correctly but with minor parameter issues or extra unnecessary calls. **Score 0.5**: Agent used some gws co...
- [ ] Agent listed unread emails using gws - [ ] Agent read individual messages using gws - [ ] Agent created a draft reply for the most urgent email - [ ] File `triage_report.md` created in workspace - [ ] All unread messages are present in the report - [ ] Each message has a priority assigned (P0-P3) - [ ] Each messa...
{"automated": 0.5, "llm_judge": 0.5}
false
[]
["npm:@juppytt/fws", "cli:gws"]
[]
[]
--- id: task_gws_email_triage name: GWS Email Triage category: integrations grading_type: hybrid timeout_seconds: 300 grading_weights: automated: 0.5 llm_judge: 0.5 workspace_files: [] prerequisites: - npm:@juppytt/fws - cli:gws --- ## Prompt You have access to a Google Workspace account through the `gws` CLI...
task_gws_task_management
GWS Task Management
integrations
hybrid
300
You have access to a Google Workspace account through the `gws` CLI tool (`gws --help` for usage). Manage your tasks based on what's in your inbox: 1. Check your current task list and mark any already-completed items as done 2. Read your recent emails to find action items 3. Create a new task for each action item you...
The agent should: 1. Use gws Tasks to list current tasks and update completed ones 2. Use gws Gmail to read recent messages and extract action items 3. Create new tasks from the action items found in emails 4. Write a summary to `task_summary.md` This tests the agent's ability to combine Tasks and Gmail, extract stru...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the GWS task management task. """ from pathlib import Path scores = {} workspace = Path(workspace_path) listed_tasks = False updated_task = False read_emails = False created_task = False def extr...
### Criterion 1: Information Extraction (Weight: 40%) **Score 1.0**: Agent correctly identified actionable items from emails and created well-titled, specific tasks for each. **Score 0.75**: Agent found most action items with reasonable task titles. **Score 0.5**: Agent found some action items but missed important one...
- [ ] Agent listed existing tasks using gws - [ ] Agent marked completed task(s) as done - [ ] Agent read emails using gws - [ ] At least one new task created from email action items - [ ] New tasks have meaningful titles derived from email content - [ ] File `task_summary.md` created with a summary
{"automated": 0.6, "llm_judge": 0.4}
false
[]
["npm:@juppytt/fws", "cli:gws"]
[]
[]
--- id: task_gws_task_management name: GWS Task Management category: integrations grading_type: hybrid timeout_seconds: 300 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: [] prerequisites: - npm:@juppytt/fws - cli:gws --- ## Prompt You have access to a Google Workspace account through the `gw...
task_humanizer
Humanize AI-Generated Blog
writing
llm_judge
120
I have a blog post in `ai_blog.txt` that sounds way too robotic and AI-generated. First, install the "humanizer" skill from the skill registry using `/install humanizer`, then use it to make the text sound more natural and human-written. If the skill isn't available, you can manually rewrite it to sound more human. Sav...
The agent should: 1. Read the provided AI-generated blog from `ai_blog.txt` 2. Use a humanizer skill/tool to transform the content 3. Save the humanized output to `humanized_blog.txt` The humanizer should address common AI writing patterns such as: - Overuse of transitional phrases ("Furthermore," "Moreover," "In co...
### Criterion 1: Skill Usage or Manual Rewrite (Weight: 25%) **Score 1.0**: Agent correctly installed and used a humanizer skill, OR performed a quality manual rewrite. **Score 0.75**: Agent attempted to install/use a skill and fell back to manual rewrite appropriately. **Score 0.5**: Agent attempted the task but with...
- [ ] Agent reads the input blog file - [ ] Agent uses a humanizer skill/tool - [ ] Output file is created with humanized content - [ ] Content maintains the original meaning and key points - [ ] Writing sounds more natural and human-like - [ ] AI-typical phrases are reduced or eliminated ---
{}
false
[]
[]
[{"source": "ai_blog.txt", "dest": "ai_blog.txt"}]
["ai_blog.txt"]
H4sIAAAAAAAC/+1XXW/kuBH0s38Fg31IAowH9mX39nn3skkOuEUWaweHPAWU1DPimhJ1JDVj36+/qqY0X3bylrxEBA57Hklkd1V1ddO6f1U+bNf5KV/9t9Yt1vdv3+q/WBf/3t2+v3s//1Z+v7u9u7u9MrdX/4M1pmwjjr/6/1xvzHvzJewlbkZv7nO0WbZOksnBfAwhZfPPMEbzJYZmrLPbufxsbN+YD3XrZCfl6V+D9en6+sceXzX2+ffJbGzKN4OtpTH7EH2zMsPpDq1NppI6dGK6EMW4bggx2z6b3NreYOOI5xs8Wpsfs3HJSErS...
--- id: task_humanizer name: Humanize AI-Generated Blog category: writing grading_type: llm_judge timeout_seconds: 120 workspace_files: - source: ai_blog.txt dest: ai_blog.txt --- # Task: Humanize AI-Generated Blog ## Prompt I have a blog post in `ai_blog.txt` that sounds way too robotic and AI-generated. Firs...
task_image_gen
AI Image Generation
skills
hybrid
120
Generate an image of a friendly robot sitting in a cozy coffee shop, reading a book. Save it as "robot_cafe.png" in the current directory.
The agent should: 1. Use the `generate_image` tool (or equivalent AI image generation capability) to create an image matching the description 2. Provide a descriptive prompt that captures the key elements: robot, coffee shop setting, cozy atmosphere, reading a book 3. Save the generated image to the specified filename...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the AI image generation task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to scores...
### Criterion 1: Image Quality and Relevance (Weight: 40%) **Score 1.0**: Generated image clearly depicts a robot in a coffee shop setting, reading a book. The scene feels cozy and all requested elements are present and well-integrated. **Score 0.75**: Image contains most elements (robot, cafe, book) but one element i...
- [ ] Agent used an image generation tool - [ ] Generated prompt includes robot - [ ] Generated prompt includes coffee shop or cafe setting - [ ] Generated prompt includes reading or book - [ ] Image file was saved with correct filename - [ ] Agent confirmed successful generation
{}
false
[]
[]
[]
[]
--- id: task_image_gen name: AI Image Generation category: skills grading_type: hybrid timeout_seconds: 120 workspace_files: [] --- ## Prompt Generate an image of a friendly robot sitting in a cozy coffee shop, reading a book. Save it as "robot_cafe.png" in the current directory. ## Expected Behavior The agent shou...
task_image_identification
Image Identification (Phone, Food, Menu)
analysis
automated
150
I placed three unlabeled images in `images/`: - `images/item_a.jpg` - `images/item_b.jpg` - `images/item_c.jpg` Classify each image into one of these categories: - `phone` - `food` - `menu` Then create a file named `image_categories.json` in the current directory with this exact JSON shape: ```json { "phone": "i...
The agent should inspect all three images and map them to the requested categories. Acceptable category meanings: - `phone`: a smartphone/device photo - `food`: an edible food item - `menu`: a paper menu-like document (receipt-style paper is acceptable) The final output should be valid JSON in `image_categories.json...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import json scores = { "file_created": 0.0, "valid_json_shape": 0.0, "has_required_categories": 0.0, "values_are_valid_paths": 0.0, "uses_each_image_once": 0.0, "phon...
- [ ] Output file `image_categories.json` is created - [ ] Output is valid JSON and follows required shape - [ ] All three required categories are present (`phone`, `food`, `menu`) - [ ] Each category maps to one of the provided image paths - [ ] Each provided image path is used exactly once - [ ] `phone` classificatio...
{}
false
[]
[]
[{"source": "images/img_0e5d267af744.jpg", "dest": "images/item_a.jpg"}, {"source": "images/img_128030b75c71.jpg", "dest": "images/item_b.jpg"}, {"source": "images/img_ddabd275f36a.jpg", "dest": "images/item_c.jpg"}]
["images/img_0e5d267af744.jpg", "images/img_128030b75c71.jpg", "images/img_ddabd275f36a.jpg"]
H4sIAAAAAAAC/+S3ZVAcThAviDsJElyCu7u7uwRZYNEFwi7uHjy4u7stu7i7u7uEBHcIHiDJ//JO6t77fFf35X4jXdU901I109Pj4GRlb+vB7eBkb8FjKwjiExK2shMWEOACu9oj/L8Fnn8QEhD43+k//K9UUJhHSJj3/+L9H3xePn5+PgQaHoT/D+Dl4Wnl/s88wv8/8d/Gf9sI8gho/4COho6BgY6F/a+9f4eD8+49MR7+v0ZMR0RG9K/TUTNQU/4bQmxMTGxCUgJ8AlIaCgoaFiYWgYFV/0MJ4v9jJUMI+BgoCCgIyIh0CEj4iMj4...
--- id: task_image_identification name: Image Identification (Phone, Food, Menu) category: analysis grading_type: automated timeout_seconds: 150 workspace_files: - source: images/img_0e5d267af744.jpg dest: images/item_a.jpg - source: images/img_128030b75c71.jpg dest: images/item_b.jpg - source: images/img...
task_it_procurement
IT Procurement Research
research
llm_judge
300
A growing startup (50 engineers) needs to purchase **developer laptops** for new hires. Help them research options and make a recommendation. **Requirements:** - 32 GB RAM minimum - 512 GB SSD minimum - Modern CPU (within last 2 generations) - Good build quality (daily professional use) - Budget: $1,500–$2,500 per uni...
The agent should: 1. Research current laptop models meeting the specifications 2. Find specific configurations with prices 3. Check Linux compatibility for non-Mac options 4. Consider enterprise/bulk purchasing factors 5. Produce a practical procurement document 6. Save to `laptop_procurement.md`
### Criterion 1: Product Research Quality (Weight: 30%) **Score 1.0**: Specific model numbers with exact configurations (e.g., "ThinkPad T14s Gen 5 — AMD Ryzen 7 PRO 8840U, 32GB, 512GB, 14\" 2.8K"). Current pricing from identifiable sources. Models are all currently available for purchase. **Score 0.75**: Good specifi...
- [ ] File `laptop_procurement.md` created - [ ] At least 5 specific laptop models documented - [ ] Prices included for each model - [ ] Specifications meet the stated requirements - [ ] Linux compatibility discussed for non-Mac options - [ ] Comparison table present - [ ] Separate macOS and Linux recommendations - [ ]...
{}
false
[]
[]
[]
[]
--- id: task_it_procurement name: IT Procurement Research category: research grading_type: llm_judge timeout_seconds: 300 workspace_files: [] --- ## Prompt A growing startup (50 engineers) needs to purchase **developer laptops** for new hires. Help them research options and make a recommendation. **Requirements:** -...
task_iterative_code_refine
Iterative Code Refinement
coding
automated
300
This is a multi-session task. See the `sessions` field in the frontmatter for the sequence of prompts.
The agent should: 1. **Session 1 (Initial Implementation)**: - Create `calculator.py` with add, subtract, multiply, and divide functions - The initial divide function should simply return a / b without error handling 2. **Session 2 (Add Error Handling)**: - Modify the existing `calculator.py` to add error ha...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the iterative code refinement task. Checks calculator.py for required functions and error handling, and review.txt for verification content. """ from pathlib import Path import re scores = {} workspace = ...
- [ ] `calculator.py` exists with add, subtract, multiply functions - [ ] `calculator.py` has divide function with ValueError for zero - [ ] `calculator.py` has power function - [ ] `calculator.py` has modulo function with ValueError for zero - [ ] `review.txt` exists and mentions all 6 functions - [ ] `review.txt` men...
{}
true
[{"id": "initial_implementation", "prompt": "Create a Python script called `calculator.py` that implements a simple calculator with these functions:\n1. `add(a, b)` \u2014 returns a + b\n2. `subtract(a, b)` \u2014 returns a - b\n3. `multiply(a, b)` \u2014 returns a * b\n4. `divide(a, b)` \u2014 returns a / b (no error ...
[]
[]
[]
--- id: task_iterative_code_refine name: Iterative Code Refinement category: coding grading_type: automated timeout_seconds: 300 multi_session: true sessions: - id: initial_implementation prompt: | Create a Python script called `calculator.py` that implements a simple calculator with these functions: ...
task_k8s_debugging
K8s/IaC Debugging
coding
automated
120
The file `deployment.yml` in the workspace contains Kubernetes manifests for a web API Deployment and its Service. The manifests have several bugs that would cause problems in a real cluster. Identify and fix all the issues, saving the corrected version in place. Known symptoms reported by the team: 1. Pods never rea...
The agent should read `deployment.yml`, identify the four bugs, and fix them: 1. **Selector mismatch**: Change `selector.matchLabels.app` from `web-api-frontend` to `web-api` so it matches the pod template's `labels.app: web-api`. 2. **Memory limit < request**: The memory limit (`64Mi`) is lower than the request (`128...
```python def grade(transcript: list, workspace_path: str) -> dict: from pathlib import Path import re scores = { "file_exists": 0.0, "valid_yaml": 0.0, "selector_matches_labels": 0.0, "memory_limit_valid": 0.0, "secret_reference_used": 0.0, "service_targetpo...
- [ ] File `deployment.yml` exists after edit - [ ] File is valid YAML - [ ] Selector matchLabels matches pod template labels - [ ] Memory limit is >= memory request - [ ] DB_PASSWORD uses a Secret reference instead of plain value - [ ] Service targetPort matches container port (8080)
{}
false
[]
[]
[{"source": "broken_k8s_deployment.yml", "dest": "deployment.yml"}]
["broken_k8s_deployment.yml"]
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--- id: task_k8s_debugging name: K8s/IaC Debugging category: coding grading_type: automated timeout_seconds: 120 workspace_files: - source: broken_k8s_deployment.yml dest: deployment.yml --- # K8s/IaC Debugging ## Prompt The file `deployment.yml` in the workspace contains Kubernetes manifests for a web API Dep...
task_log_apache_client_issues
Apache Error Log - Identify Problematic Client IPs
log_analysis
hybrid
180
Analyze the Apache error log at `apache_error.log` and identify the most problematic client IP addresses. The log is from an Apache 2.0.49 server running on Fedora, covering June 9–16, 2005. For each of the **top 5 client IPs by error count**, report: 1. The IP address 2. Total number of error entries 3. The primary ...
The agent should parse the log file and count error entries per client IP. The top 5 IPs by error count are: | Rank | IP | Error Count | Primary Activity | |---|---|---|---| | 1 | 202.133.98.6 | ~184 | Scanning for awstats/stats scripts (file not exist, script not found) | | 2 | 81.214.165.213 | ~23 | Probing for `_vt...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Apache error log client issues analysis task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "client_issues_report.md" if not report_file.exists(): retur...
- [ ] `client_issues_report.md` is created in the workspace - [ ] IP `202.133.98.6` is identified as the top problematic client (highest error count) - [ ] The report identifies `202.133.98.6` as scanning for awstats or statistics-related scripts - [ ] At least 3 of the top 5 IPs are correctly identified with approxima...
{}
false
[]
[]
[{"dest": "apache_error.log", "source": "logs/apache_error.log"}]
["logs/apache_error.log"]
H4sIAAAAAAAC/+2dbXMbN5KA8/l+xXxxnV1rQ3ifly9bSby+ym5Su3XO1V1V4nLR5FhiIg615FCS//11D2mZIoHhEMAM9QJWFCWk2A/QaDTQQANzOT9fno2uRuOL8mO5WMwX5HJ+/l3YF4WXlrL5Da/7v3mqGbt7b/0+41Sp7xL63QCv1bIeLQD/3fN8/fbrxSr5+6pKaJ5QXdC0oDLhlKoPyW/VvJ6Oyw/Jz2+//1eR/LCaXtbJzbS+SP55VVb4ZvNJ8v7tP/6ju5j3739Olqurq/miTlbV6Ho0vRx9uiw7SViu/vZ/f/sxmZXji1E1...
--- id: task_log_apache_client_issues name: Apache Error Log - Identify Problematic Client IPs category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "apache_error.log" source: "logs/apache_error.log" --- # Apache Error Log - Identify Problematic Client IPs ## Prompt Analyze ...
task_log_apache_critical
Apache Error Log - Identify Critical Security Issues
log_analysis
hybrid
180
You are a security analyst reviewing the Apache error log at `apache_error.log`. Your job is to identify all **security-relevant** entries — things that indicate active attacks, vulnerability scanning, or exploitation attempts against this server. Classify each finding into one of these severity levels: | Severity | ...
The agent should identify at least these findings: **Critical:** - **IIS directory traversal / command execution attempts**: Multiple IPs sending "Invalid method" requests containing paths like `/scripts/..%c0%af../winnt/system32/cmd.exe?/c+dir`. These use Unicode encoding exploits (CVE-2000-0884, CVE-2001-0333) to at...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Apache error log critical security issues task.""" from pathlib import Path import json scores = {} workspace = Path(workspace_path) report_file = workspace / "security_findings.json" if not report_file.exists...
- [ ] `security_findings.json` is created in the workspace - [ ] Command execution / directory traversal attempts identified as critical (cmd.exe, root.exe patterns) - [ ] Awstats scanning identified (202.133.98.6 or awstats keyword) - [ ] At least 3 distinct attack categories are identified - [ ] Findings use a severi...
{}
false
[]
[]
[{"dest": "apache_error.log", "source": "logs/apache_error.log"}]
["logs/apache_error.log"]
H4sIAAAAAAAC/+2dbXMbN5KA8/l+xXxxnV1rQ3ifly9bSby+ym5Su3XO1V1V4nLR5FhiIg615FCS//11D2mZIoHhEMAM9QJWFCWk2A/QaDTQQANzOT9fno2uRuOL8mO5WMwX5HJ+/l3YF4WXlrL5Da/7v3mqGbt7b/0+41Sp7xL63QCv1bIeLQD/3fN8/fbrxSr5+6pKaJ5QXdC0oDLhlKoPyW/VvJ6Oyw/Jz2+//1eR/LCaXtbJzbS+SP55VVb4ZvNJ8v7tP/6ju5j3739Olqurq/miTlbV6Ho0vRx9uiw7SViu/vZ/f/sxmZXji1E1...
--- id: task_log_apache_critical name: Apache Error Log - Identify Critical Security Issues category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "apache_error.log" source: "logs/apache_error.log" --- # Apache Error Log - Identify Critical Security Issues ## Prompt You are a...
task_log_apache_error_summary
Apache Error Log - Generate Error Summary Report
log_analysis
hybrid
180
Analyze the Apache error log at `apache_error.log` and produce a comprehensive summary report. The log is from an Apache 2.0.49 server on Fedora, covering approximately one week in June 2005. Your report should include the following sections: 1. **Overview**: Total log entries, date range covered, breakdown of log le...
The agent should parse the entire log and produce a report covering: **Overview:** - ~1000 total log lines - Date range: Thu Jun 9 to Thu Jun 16, 2005 - ~753 error entries, ~247 notice entries **Server Configuration Issues:** - JK connector (mod_jk/jk2) initialization failures — children not found in scoreboard - env...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Apache error log summary report task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "error_summary.md" if not report_file.exists(): return { ...
- [ ] `error_summary.md` is created in the workspace - [ ] Report includes date range and log level breakdown (error vs notice counts) - [ ] Server-side configuration issues (mod_jk, createBean) are identified separately from client errors - [ ] Security threats are identified with specific evidence (IIS worms, directo...
{}
false
[]
[]
[{"dest": "apache_error.log", "source": "logs/apache_error.log"}]
["logs/apache_error.log"]
H4sIAAAAAAAC/+2dbXMbN5KA8/l+xXxxnV1rQ3ifly9bSby+ym5Su3XO1V1V4nLR5FhiIg615FCS//11D2mZIoHhEMAM9QJWFCWk2A/QaDTQQANzOT9fno2uRuOL8mO5WMwX5HJ+/l3YF4WXlrL5Da/7v3mqGbt7b/0+41Sp7xL63QCv1bIeLQD/3fN8/fbrxSr5+6pKaJ5QXdC0oDLhlKoPyW/VvJ6Oyw/Jz2+//1eR/LCaXtbJzbS+SP55VVb4ZvNJ8v7tP/6ju5j3739Olqurq/miTlbV6Ho0vRx9uiw7SViu/vZ/f/sxmZXji1E1...
--- id: task_log_apache_error_summary name: Apache Error Log - Generate Error Summary Report category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "apache_error.log" source: "logs/apache_error.log" --- # Apache Error Log - Generate Error Summary Report ## Prompt Analyze the ...
task_log_apache_timeline
Apache Error Log - Create Error Timeline
log_analysis
hybrid
180
Analyze the Apache error log at `apache_error.log` and create a timeline of significant events. The log spans from Thursday June 9 to Thursday June 16, 2005. For each day, identify: 1. The number of error-level entries 2. Notable events (server restarts, attack bursts, unusual activity spikes) 3. Any periods of conce...
The agent should parse timestamps and group entries by day. Expected daily breakdown (approximate): | Date | Day | Error Count | Notable Events | |---|---|---|---| | Jun 9 | Thu | ~50 | Server startup, JK connector errors, directory scanning begins | | Jun 10 | Fri | ~80 | Server restart at 11:32, IIS worm probes (Inv...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Apache error log timeline task.""" from pathlib import Path import json scores = {} workspace = Path(workspace_path) report_file = workspace / "error_timeline.json" if not report_file.exists(): return ...
- [ ] `error_timeline.json` is created in the workspace - [ ] Daily breakdown covers at least 5 of the 8 days with error counts - [ ] June 11 (Saturday) is identified as the day with the most errors - [ ] The peak burst is attributed to 202.133.98.6 or awstats scanning around 03:03 on June 11 - [ ] Server restart event...
{}
false
[]
[]
[{"dest": "apache_error.log", "source": "logs/apache_error.log"}]
["logs/apache_error.log"]
H4sIAAAAAAAC/+2dbXMbN5KA8/l+xXxxnV1rQ3ifly9bSby+ym5Su3XO1V1V4nLR5FhiIg615FCS//11D2mZIoHhEMAM9QJWFCWk2A/QaDTQQANzOT9fno2uRuOL8mO5WMwX5HJ+/l3YF4WXlrL5Da/7v3mqGbt7b/0+41Sp7xL63QCv1bIeLQD/3fN8/fbrxSr5+6pKaJ5QXdC0oDLhlKoPyW/VvJ6Oyw/Jz2+//1eR/LCaXtbJzbS+SP55VVb4ZvNJ8v7tP/6ju5j3739Olqurq/miTlbV6Ho0vRx9uiw7SViu/vZ/f/sxmZXji1E1...
--- id: task_log_apache_timeline name: Apache Error Log - Create Error Timeline category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "apache_error.log" source: "logs/apache_error.log" --- # Apache Error Log - Create Error Timeline ## Prompt Analyze the Apache error log at `...
task_log_apache_top_errors
Apache Error Log - Rank Top Error Types
log_analysis
hybrid
180
Analyze the Apache error log at `apache_error.log` and categorize all `[error]`-level entries by error type. Ignore `[notice]`-level entries. Produce a ranked list of error types from most to least frequent. For each error type, provide: 1. A descriptive name for the error category 2. The exact count of occurrences 3...
The agent should parse all `[error]`-level lines and group them by error message pattern. The expected top error categories are: | Rank | Error Type | Count | |---|---|---| | 1 | Directory index forbidden by rule | ~224 | | 2 | File does not exist (various paths) | ~200+ | | 3 | script not found or unable to stat | ~6...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Apache error log top errors ranking task.""" from pathlib import Path import json scores = {} workspace = Path(workspace_path) report_file = workspace / "error_types_report.json" if not report_file.exists(): ...
- [ ] `error_types_report.json` is created in the workspace - [ ] "Directory index forbidden" is identified as the most frequent client error (~224) - [ ] "File does not exist" errors are identified and counted - [ ] "Invalid method in request" is identified as a distinct error type - [ ] Server-internal errors (mod_jk...
{}
false
[]
[]
[{"dest": "apache_error.log", "source": "logs/apache_error.log"}]
["logs/apache_error.log"]
H4sIAAAAAAAC/+2dbXMbN5KA8/l+xXxxnV1rQ3ifly9bSby+ym5Su3XO1V1V4nLR5FhiIg615FCS//11D2mZIoHhEMAM9QJWFCWk2A/QaDTQQANzOT9fno2uRuOL8mO5WMwX5HJ+/l3YF4WXlrL5Da/7v3mqGbt7b/0+41Sp7xL63QCv1bIeLQD/3fN8/fbrxSr5+6pKaJ5QXdC0oDLhlKoPyW/VvJ6Oyw/Jz2+//1eR/LCaXtbJzbS+SP55VVb4ZvNJ8v7tP/6ju5j3739Olqurq/miTlbV6Ho0vRx9uiw7SViu/vZ/f/sxmZXji1E1...
--- id: task_log_apache_top_errors name: Apache Error Log - Rank Top Error Types category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "apache_error.log" source: "logs/apache_error.log" --- # Apache Error Log - Rank Top Error Types ## Prompt Analyze the Apache error log at `...
task_log_hdfs_block_ops
HDFS DataNode Log - Block Operations Summary
log_analysis
hybrid
180
Analyze the HDFS DataNode log at `hdfs_datanode.log` and produce a comprehensive summary of all block operations. The log comes from an HDFS cluster and tracks block lifecycle events. Your report should include: 1. **Block Inventory**: Total unique block IDs in the log, with a full list 2. **Operation Types**: For ea...
The agent should parse 2000 log entries and produce: **Block Inventory:** - ~390 unique block IDs **Operation Counts:** - Receiving block: ~1149 - allocateBlock: ~385 - Received block: ~19 - addStoredBlock: ~19 - PacketResponder: ~12 - Replicate: 4 **Complete Block Lifecycles (blocks with full data):** - blk_-160899...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the HDFS block operations summary task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "hdfs_block_ops_report.md" if not report_file.exists(): return { ...
- [ ] `hdfs_block_ops_report.md` is created in the workspace - [ ] Unique block count is provided (~390) - [ ] Operation types are counted (receiving, allocate, replicate, etc.) - [ ] At least one block lifecycle is fully traced (allocate → receive → stored) - [ ] The associated MapReduce job is identified (job_2008110...
{}
false
[]
[]
[{"dest": "hdfs_datanode.log", "source": "logs/hdfs_datanode.log"}]
["logs/hdfs_datanode.log"]
H4sIAAAAAAAC/+S9y64kSZIl1msC/IdYcEESk176ftzlkBiAIKeHmJ4Fd4WYzqjunK6qLGRGE5z5+jlibuZXw0TUXVXt4QYwu6qzqizy6rlqavJSkXP+/Os//f6Hf/75T7//8eev37/+9defv93+/Os//d2ufyn8FZyb/o6/fvi7ViH64Jf/7f6/a2OM/bsv6u9O+Otff//+9Tcs/3f///xLJa1V/mKU9Tp90c5++T/+/t/9hy84ELf/HQfi73Eg/if6D//PP3775f/99tvHl//4jf7TL3/9py//+c+//uO/4P//yx9/0kGlnHNIMeuc...
--- id: task_log_hdfs_block_ops name: HDFS DataNode Log - Block Operations Summary category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "hdfs_datanode.log" source: "logs/hdfs_datanode.log" --- # HDFS DataNode Log - Block Operations Summary ## Prompt Analyze the HDFS DataNod...
task_log_hdfs_connections
HDFS DataNode Log - Connection Pattern Analysis
log_analysis
hybrid
180
Analyze the HDFS DataNode log at `hdfs_datanode.log` and produce a report on connection and communication patterns between nodes. The log contains entries from DataNode, FSNamesystem, and PacketResponder components. Your report should include: 1. **Network Topology**: List all unique IP addresses that appear in the l...
The agent should parse 2000 log entries and produce: **Network Topology:** - 202 unique IP addresses observed - IPs fall in the 10.250.x.x and 10.251.x.x ranges (private network) - All nodes use port 50010 (HDFS DataNode data transfer port) **Subnet Analysis:** - 10.250.x.x subnet — contains some of the most active n...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the HDFS connection pattern analysis task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "hdfs_connections_report.md" if not report_file.exists(): return { ...
- [ ] `hdfs_connections_report.md` is created in the workspace - [ ] Unique IPs are listed or counted (~202) - [ ] IPs are grouped by subnet (10.250.x.x vs 10.251.x.x) - [ ] Most active nodes are identified - [ ] DataNode vs FSNamesystem activity is distinguished ---
{}
false
[]
[]
[{"dest": "hdfs_datanode.log", "source": "logs/hdfs_datanode.log"}]
["logs/hdfs_datanode.log"]
H4sIAAAAAAAC/+S9y64kSZIl1msC/IdYcEESk176ftzlkBiAIKeHmJ4Fd4WYzqjunK6qLGRGE5z5+jlibuZXw0TUXVXt4QYwu6qzqizy6rlqavJSkXP+/Os//f6Hf/75T7//8eev37/+9defv93+/Os//d2ufyn8FZyb/o6/fvi7ViH64Jf/7f6/a2OM/bsv6u9O+Otff//+9Tcs/3f///xLJa1V/mKU9Tp90c5++T/+/t/9hy84ELf/HQfi73Eg/if6D//PP3775f/99tvHl//4jf7TL3/9py//+c+//uO/4P//yx9/0kGlnHNIMeuc...
--- id: task_log_hdfs_connections name: HDFS DataNode Log - Connection Pattern Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "hdfs_datanode.log" source: "logs/hdfs_datanode.log" --- # HDFS DataNode Log - Connection Pattern Analysis ## Prompt Analyze the HDFS...
task_log_hdfs_failures
HDFS DataNode Log - Block and Replication Failure Analysis
log_analysis
hybrid
180
Analyze the HDFS DataNode log at `hdfs_datanode.log` and identify any block operation failures, replication issues, or error conditions. The log is from an HDFS cluster and contains DataNode, FSNamesystem, and PacketResponder entries. Your report should include: 1. **Log Overview**: Total entries, date/time range, lo...
The agent should parse 2000 log entries and produce: **Log Overview:** - 2000 entries, all from November 9, 2008 (081109), covering ~28 seconds (203518–203546) - All entries are INFO level — no WARN or ERROR entries **Block Operations:** - Receiving block: ~1149 entries - Allocate block: ~385 entries - Received block...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the HDFS failure analysis task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "hdfs_failure_report.md" if not report_file.exists(): return { "ou...
- [ ] `hdfs_failure_report.md` is created in the workspace - [ ] Log overview with entry count and time range is provided - [ ] Block operations are categorized and counted (receive, allocate, replicate) - [ ] The absence of WARN/ERROR entries is noted (or any found are detailed) - [ ] A health assessment is provided ...
{}
false
[]
[]
[{"dest": "hdfs_datanode.log", "source": "logs/hdfs_datanode.log"}]
["logs/hdfs_datanode.log"]
H4sIAAAAAAAC/+S9y64kSZIl1msC/IdYcEESk176ftzlkBiAIKeHmJ4Fd4WYzqjunK6qLGRGE5z5+jlibuZXw0TUXVXt4QYwu6qzqizy6rlqavJSkXP+/Os//f6Hf/75T7//8eev37/+9defv93+/Os//d2ufyn8FZyb/o6/fvi7ViH64Jf/7f6/a2OM/bsv6u9O+Otff//+9Tcs/3f///xLJa1V/mKU9Tp90c5++T/+/t/9hy84ELf/HQfi73Eg/if6D//PP3775f/99tvHl//4jf7TL3/9py//+c+//uO/4P//yx9/0kGlnHNIMeuc...
--- id: task_log_hdfs_failures name: HDFS DataNode Log - Block and Replication Failure Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "hdfs_datanode.log" source: "logs/hdfs_datanode.log" --- # HDFS DataNode Log - Block and Replication Failure Analysis ## Promp...
task_log_hdfs_slow_ops
HDFS DataNode Log - Slow Operation Detection
log_analysis
hybrid
180
Analyze the HDFS DataNode log at `hdfs_datanode.log` and identify operations that took longer than expected. The log records block receives, allocations, and replications with timestamps. Your report should include: 1. **Block Lifecycle Timing**: For blocks where both "Receiving block" and "Received block" entries ex...
The agent should parse timestamps from the log format `YYMMDD HHMMSS` and calculate: **Block Lifecycle:** - The log covers only ~28 seconds (203518 to 203546) - Most block operations complete within 1-3 seconds - Confirmed block receives with sizes: 91178 bytes, 233217 bytes, 11971 bytes, 11977 bytes **Key observatio...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the HDFS slow operation detection task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "hdfs_slow_ops_report.md" if not report_file.exists(): return { ...
- [ ] `hdfs_slow_ops_report.md` is created in the workspace - [ ] Block lifecycle timing is calculated for at least one block - [ ] Block sizes are correlated with operation times where data is available - [ ] The time range of the log is correctly identified (~28 seconds) - [ ] A performance assessment is provided --...
{}
false
[]
[]
[{"dest": "hdfs_datanode.log", "source": "logs/hdfs_datanode.log"}]
["logs/hdfs_datanode.log"]
H4sIAAAAAAAC/+S9y64kSZIl1msC/IdYcEESk176ftzlkBiAIKeHmJ4Fd4WYzqjunK6qLGRGE5z5+jlibuZXw0TUXVXt4QYwu6qzqizy6rlqavJSkXP+/Os//f6Hf/75T7//8eev37/+9defv93+/Os//d2ufyn8FZyb/o6/fvi7ViH64Jf/7f6/a2OM/bsv6u9O+Otff//+9Tcs/3f///xLJa1V/mKU9Tp90c5++T/+/t/9hy84ELf/HQfi73Eg/if6D//PP3775f/99tvHl//4jf7TL3/9py//+c+//uO/4P//yx9/0kGlnHNIMeuc...
--- id: task_log_hdfs_slow_ops name: HDFS DataNode Log - Slow Operation Detection category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "hdfs_datanode.log" source: "logs/hdfs_datanode.log" --- # HDFS DataNode Log - Slow Operation Detection ## Prompt Analyze the HDFS DataNode...
task_log_hdfs_storage
HDFS DataNode Log - Storage and Capacity Analysis
log_analysis
hybrid
180
Analyze the HDFS DataNode log at `hdfs_datanode.log` and produce a storage-focused analysis. Examine block sizes, data distribution across nodes, and storage patterns. Your report should include: 1. **Data Volume**: Total bytes stored across all confirmed block receives (where size is known) 2. **Block Size Distribut...
The agent should parse the log and calculate: **Confirmed Block Sizes:** - blk_-1608999687919862906: 91,178 bytes (received by 3+ nodes) - blk_7503483334202473044: 233,217 bytes (received by 3 nodes) - blk_-3544583377289625738: 11,971 bytes (received by 3 nodes) - blk_-9073992586687739851: 11,977 bytes (received by 3 ...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the HDFS storage and capacity analysis task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "hdfs_storage_report.md" if not report_file.exists(): return { ...
- [ ] `hdfs_storage_report.md` is created in the workspace - [ ] Known block sizes are listed (91178, 233217, 11971, 11977) - [ ] Block size statistics are calculated (min, max, mean) - [ ] Replication factor is identified (3) - [ ] Storage paths are extracted from the log ---
{}
false
[]
[]
[{"dest": "hdfs_datanode.log", "source": "logs/hdfs_datanode.log"}]
["logs/hdfs_datanode.log"]
H4sIAAAAAAAC/+S9y64kSZIl1msC/IdYcEESk176ftzlkBiAIKeHmJ4Fd4WYzqjunK6qLGRGE5z5+jlibuZXw0TUXVXt4QYwu6qzqizy6rlqavJSkXP+/Os//f6Hf/75T7//8eev37/+9defv93+/Os//d2ufyn8FZyb/o6/fvi7ViH64Jf/7f6/a2OM/bsv6u9O+Otff//+9Tcs/3f///xLJa1V/mKU9Tp90c5++T/+/t/9hy84ELf/HQfi73Eg/if6D//PP3775f/99tvHl//4jf7TL3/9py//+c+//uO/4P//yx9/0kGlnHNIMeuc...
--- id: task_log_hdfs_storage name: HDFS DataNode Log - Storage and Capacity Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "hdfs_datanode.log" source: "logs/hdfs_datanode.log" --- # HDFS DataNode Log - Storage and Capacity Analysis ## Prompt Analyze the HDFS...
task_log_mapreduce_failures
MapReduce Log - Failed Task Analysis
log_analysis
hybrid
180
Analyze the Hadoop MapReduce application log at `mapreduce.log` and identify any task failures, errors, or anomalies. Focus on anything that went wrong during execution. Your report should include: 1. **Error and Warning Entries**: List all WARN and ERROR level log entries with full context 2. **Task Retries**: Ident...
The agent should identify: **WARN Entries (4 total):** 1. ResponseProcessor for block BP-1347369012-10.190.173.170-1444972147527:blk_1073742514_1708 — related to I/O issue 2. DataStreamer for file /tmp/hadoop-yarn/staging/msrabi/.staging/job_1445062781478_0011/job — write pipeline issue 3. CommitterEvent Processor — F...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the MapReduce failed task analysis.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "mapreduce_failures.md" if not report_file.exists(): return { ...
- [ ] `mapreduce_failures.md` is created in the workspace - [ ] WARN and ERROR entries are listed (4 WARN, 1 ERROR) - [ ] Task retries are identified (m_000006 and m_000007 retried) - [ ] The IOException / bad response error is analyzed - [ ] Impact assessment notes the job still succeeded ---
{}
false
[]
[]
[{"dest": "mapreduce.log", "source": "logs/hadoop_mapreduce.log"}]
["logs/hadoop_mapreduce.log"]
H4sIAAAAAAAC/+29fXMix7InfP7dE3G/Q28cR6y9gZh6f2GvHY+skT3yGWlmJc31bpzjUDDQkvAg4EIzts4T+903qxtQM1NVNBINiVbcO8cCmu5fZmXlS1VmVn94M3l12+4Oh6Oru/ZonHannbTZH978ZXMvAi8lRP5feC3/VzPBmJ5/VnxOmeb0Lwn5yxZe00nWHsPj//L/5osRKg8oOaA6obLFdUuqhhQ6OTn76V3yj7t2b/BbMhzfNNujduc2bRaS0nyQlM8Mvho1T88PR6PT9iRLx63kaJy2s7SblD5MrofjBC7s9zrtrDccuL/b...
--- id: task_log_mapreduce_failures name: MapReduce Log - Failed Task Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "mapreduce.log" source: "logs/hadoop_mapreduce.log" --- # MapReduce Log - Failed Task Analysis ## Prompt Analyze the Hadoop MapReduce applicat...
task_log_mapreduce_jobs
MapReduce Log - Job Completion Summary
log_analysis
hybrid
180
Analyze the Hadoop MapReduce application log at `mapreduce.log` and produce a comprehensive job completion summary. The log is from a MapReduce v2 (YARN) application. Your report should include: 1. **Job Identification**: Job ID, application attempt ID, and job name/type 2. **Job Configuration**: OutputCommitter type...
The agent should parse 1282 log entries and produce: **Job Identification:** - Job ID: job_1445062781478_0011 - Application Attempt: appattempt_1445062781478_0011_000001 - Job type: pagerank (visible in history file path) - User: msrabi **Configuration:** - OutputCommitter: FileOutputCommitter - File system: hdfs://m...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the MapReduce job completion summary task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "job_completion_report.md" if not report_file.exists(): return { ...
- [ ] `job_completion_report.md` is created in the workspace - [ ] Job ID (job_1445062781478_0011) is identified - [ ] Map and reduce task counts are correct (10 map tasks, 1 reduce task) - [ ] Job duration is calculated (~5 minutes) - [ ] Final status is identified as SUCCEEDED ---
{}
false
[]
[]
[{"dest": "mapreduce.log", "source": "logs/hadoop_mapreduce.log"}]
["logs/hadoop_mapreduce.log"]
H4sIAAAAAAAC/+29fXMix7InfP7dE3G/Q28cR6y9gZh6f2GvHY+skT3yGWlmJc31bpzjUDDQkvAg4EIzts4T+903qxtQM1NVNBINiVbcO8cCmu5fZmXlS1VmVn94M3l12+4Oh6Oru/ZonHannbTZH978ZXMvAi8lRP5feC3/VzPBmJ5/VnxOmeb0Lwn5yxZe00nWHsPj//L/5osRKg8oOaA6obLFdUuqhhQ6OTn76V3yj7t2b/BbMhzfNNujduc2bRaS0nyQlM8Mvho1T88PR6PT9iRLx63kaJy2s7SblD5MrofjBC7s9zrtrDccuL/b...
--- id: task_log_mapreduce_jobs name: MapReduce Log - Job Completion Summary category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "mapreduce.log" source: "logs/hadoop_mapreduce.log" --- # MapReduce Log - Job Completion Summary ## Prompt Analyze the Hadoop MapReduce applicat...
task_log_mapreduce_resources
MapReduce Log - Resource Utilization Analysis
log_analysis
hybrid
180
Analyze the Hadoop MapReduce application log at `mapreduce.log` and produce a resource utilization report. Focus on container allocation, scheduling, and resource usage patterns. Your report should include: 1. **Container Inventory**: List all containers allocated for this job, with their IDs 2. **Container Allocatio...
The agent should parse RMContainerAllocator entries and produce: **Container Inventory:** - 13 unique containers used (container_1445062781478_0011_01_000001 through 000013) - Application attempt: 01 **Scheduling Progression:** - Initial state: 10 pending maps, 1 pending reduce - Reduce slow start threshold repeatedl...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the MapReduce resource utilization analysis task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "mapreduce_resources.md" if not report_file.exists(): return...
- [ ] `mapreduce_resources.md` is created in the workspace - [ ] Containers are listed (13 containers identified) - [ ] Scheduling progression is tracked (pending maps/reduces over time) - [ ] Reduce slow start threshold discussion is included - [ ] Container completion events are analyzed ---
{}
false
[]
[]
[{"dest": "mapreduce.log", "source": "logs/hadoop_mapreduce.log"}]
["logs/hadoop_mapreduce.log"]
H4sIAAAAAAAC/+29fXMix7InfP7dE3G/Q28cR6y9gZh6f2GvHY+skT3yGWlmJc31bpzjUDDQkvAg4EIzts4T+903qxtQM1NVNBINiVbcO8cCmu5fZmXlS1VmVn94M3l12+4Oh6Oru/ZonHannbTZH978ZXMvAi8lRP5feC3/VzPBmJ5/VnxOmeb0Lwn5yxZe00nWHsPj//L/5osRKg8oOaA6obLFdUuqhhQ6OTn76V3yj7t2b/BbMhzfNNujduc2bRaS0nyQlM8Mvho1T88PR6PT9iRLx63kaJy2s7SblD5MrofjBC7s9zrtrDccuL/b...
--- id: task_log_mapreduce_resources name: MapReduce Log - Resource Utilization Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "mapreduce.log" source: "logs/hadoop_mapreduce.log" --- # MapReduce Log - Resource Utilization Analysis ## Prompt Analyze the Hadoop...
task_log_mapreduce_slow_tasks
MapReduce Log - Slow Task Identification
log_analysis
hybrid
180
Analyze the Hadoop MapReduce application log at `mapreduce.log` and identify which map and reduce tasks were slowest. Compare task completion times to find stragglers. Your report should include: 1. **Task Completion Times**: For each completed task, calculate the time from container assignment to task completion 2. ...
The agent should extract task completion timestamps and calculate: **Map Task Completions (in order):** 1. m_000009: completed 15:39:24 (first) 2. m_000005: completed 15:40:28 3. m_000003: completed 15:40:32 4. m_000000: completed 15:40:34 5. m_000001: completed 15:40:50 6. m_000002: completed 15:40:50 7. m_000004: co...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the MapReduce slow task identification task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "slow_tasks_report.md" if not report_file.exists(): return { ...
- [ ] `slow_tasks_report.md` is created in the workspace - [ ] Individual task completion times are listed - [ ] Fastest and slowest map tasks are identified - [ ] Retried tasks (m_000006, m_000007) are flagged as slower - [ ] The reduce task timing is analyzed separately ---
{}
false
[]
[]
[{"dest": "mapreduce.log", "source": "logs/hadoop_mapreduce.log"}]
["logs/hadoop_mapreduce.log"]
H4sIAAAAAAAC/+29fXMix7InfP7dE3G/Q28cR6y9gZh6f2GvHY+skT3yGWlmJc31bpzjUDDQkvAg4EIzts4T+903qxtQM1NVNBINiVbcO8cCmu5fZmXlS1VmVn94M3l12+4Oh6Oru/ZonHannbTZH978ZXMvAi8lRP5feC3/VzPBmJ5/VnxOmeb0Lwn5yxZe00nWHsPj//L/5osRKg8oOaA6obLFdUuqhhQ6OTn76V3yj7t2b/BbMhzfNNujduc2bRaS0nyQlM8Mvho1T88PR6PT9iRLx63kaJy2s7SblD5MrofjBC7s9zrtrDccuL/b...
--- id: task_log_mapreduce_slow_tasks name: MapReduce Log - Slow Task Identification category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "mapreduce.log" source: "logs/hadoop_mapreduce.log" --- # MapReduce Log - Slow Task Identification ## Prompt Analyze the Hadoop MapReduc...
task_log_mapreduce_timeline
MapReduce Log - Job Timeline Visualization
log_analysis
hybrid
180
Analyze the Hadoop MapReduce application log at `mapreduce.log` and create a detailed timeline visualization of the entire job execution. Show all major events in chronological order. Your output should include: 1. **Event Timeline**: A chronological list of every significant event with timestamp, including: - Job...
The agent should produce a timeline like: **Phase Breakdown:** | Phase | Start | End | Duration | |---|---|---|---| | Initialization | 15:37:56 | 15:38:00 | ~4s | | Map Phase | 15:38:00 | 15:41:25 | ~3m 25s | | Reduce Phase | 15:39:24 | 15:42:46 | ~3m 22s | | Cleanup | 15:42:46 | 15:42:47 | ~1s | | **Total** | **15:37...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the MapReduce timeline visualization task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "mapreduce_timeline.md" if not report_file.exists(): return { ...
- [ ] `mapreduce_timeline.md` is created in the workspace - [ ] Events are listed chronologically with timestamps - [ ] Phases are identified (init, map, reduce, completion) - [ ] A visual or structured timeline/gantt is attempted - [ ] Key events (first map completion, errors, job success) are highlighted ---
{}
false
[]
[]
[{"dest": "mapreduce.log", "source": "logs/hadoop_mapreduce.log"}]
["logs/hadoop_mapreduce.log"]
H4sIAAAAAAAC/+29fXMix7InfP7dE3G/Q28cR6y9gZh6f2GvHY+skT3yGWlmJc31bpzjUDDQkvAg4EIzts4T+903qxtQM1NVNBINiVbcO8cCmu5fZmXlS1VmVn94M3l12+4Oh6Oru/ZonHannbTZH978ZXMvAi8lRP5feC3/VzPBmJ5/VnxOmeb0Lwn5yxZe00nWHsPj//L/5osRKg8oOaA6obLFdUuqhhQ6OTn76V3yj7t2b/BbMhzfNNujduc2bRaS0nyQlM8Mvho1T88PR6PT9iRLx63kaJy2s7SblD5MrofjBC7s9zrtrDccuL/b...
--- id: task_log_mapreduce_timeline name: MapReduce Log - Job Timeline Visualization category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "mapreduce.log" source: "logs/hadoop_mapreduce.log" --- # MapReduce Log - Job Timeline Visualization ## Prompt Analyze the Hadoop MapRed...
task_log_nginx_errors
Nginx Access Log - Error Pattern Analysis
log_analysis
hybrid
180
Analyze the Nginx JSON access log at `nginx_access.log` and produce a detailed report on error patterns (4xx and 5xx responses). Each line is a JSON object with fields: `time`, `remote_ip`, `remote_user`, `request`, `response`, `bytes`, `referrer`, `agent`. Your report should include: 1. **Error Overview**: Total err...
The agent should parse all 1000 JSON log entries and produce: **Error Overview:** - Total errors: 690 (69.0% of all requests) - 404: 688 errors - 403: 2 errors - No 5xx errors observed **404 Analysis:** - All 404s target `/downloads/product_1` and `/downloads/product_2` - These same paths also return 200 and 304 at o...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Nginx error pattern analysis task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "error_analysis.md" if not report_file.exists(): return { "...
- [ ] `error_analysis.md` is created in the workspace - [ ] Error rate and status code breakdown are provided (690 errors, 69%, 404/403 split) - [ ] 404 errors are analyzed by path (/downloads/product_1, /downloads/product_2) - [ ] Top error-generating IPs are listed - [ ] At least 2 remediation recommendations are pro...
{}
false
[]
[]
[{"dest": "nginx_access.log", "source": "logs/nginx_access_json.log"}]
["logs/nginx_access_json.log"]
H4sIAAAAAAAC/+1dS28dx3LWOr+C0OoGiUddXf30zoCN602AuzCQpUHZjKJEohRSRCwEyW/PDA8NSEqq53A4VV1TzQMLliiDVqlfVfU96t2HN7evrt+8vf7j18vffru6vf31324/XE/vPrx5sdvHzZ8Uwv2/58/X/84uZ8h/fu30dUDA8OLCvRD43N1+uryZ//cvxvz818tPb99fvfz+4iXkV/90+fmVdxC/d+V7F79Hf/EPy4K8/MeLlzdX7z98uvr17cflP604QXFThgm/+L2726ub5Xe/O33tP+6ubj8tv/7rT79cvPr9w39ev/tw...
--- id: task_log_nginx_errors name: Nginx Access Log - Error Pattern Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "nginx_access.log" source: "logs/nginx_access_json.log" --- # Nginx Access Log - Error Pattern Analysis ## Prompt Analyze the Nginx JSON access...
task_log_nginx_slow_requests
Nginx Access Log - Find Largest Responses
log_analysis
hybrid
180
Analyze the Nginx JSON access log at `nginx_access.log` and identify the requests that generated the largest responses (by bytes transferred). Each line is a JSON object with fields: `time`, `remote_ip`, `remote_user`, `request`, `response`, `bytes`, `referrer`, `agent`. Your report should include: 1. **Top 10 Larges...
The agent should parse all 1000 JSON log entries and produce: **Top Largest Responses:** - Maximum bytes observed: ~3318 bytes - Largest responses are 200 OK responses for `/downloads/product_1` and `/downloads/product_2` - Top byte values include: 3318, 3316, 3301, 2582, 2578, etc. **Zero-Byte Analysis:** - 304 Not ...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Nginx largest responses task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "large_responses_report.md" if not report_file.exists(): return { ...
- [ ] `large_responses_report.md` is created in the workspace - [ ] Top largest responses are listed with byte counts - [ ] Zero-byte / 304 responses are analyzed separately - [ ] Distribution statistics (min, max, mean or median) are provided - [ ] Paths associated with largest responses are identified ---
{}
false
[]
[]
[{"dest": "nginx_access.log", "source": "logs/nginx_access_json.log"}]
["logs/nginx_access_json.log"]
H4sIAAAAAAAC/+1dS28dx3LWOr+C0OoGiUddXf30zoCN602AuzCQpUHZjKJEohRSRCwEyW/PDA8NSEqq53A4VV1TzQMLliiDVqlfVfU96t2HN7evrt+8vf7j18vffru6vf31324/XE/vPrx5sdvHzZ8Uwv2/58/X/84uZ8h/fu30dUDA8OLCvRD43N1+uryZ//cvxvz818tPb99fvfz+4iXkV/90+fmVdxC/d+V7F79Hf/EPy4K8/MeLlzdX7z98uvr17cflP604QXFThgm/+L2726ub5Xe/O33tP+6ubj8tv/7rT79cvPr9w39ev/tw...
--- id: task_log_nginx_slow_requests name: Nginx Access Log - Find Largest Responses category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "nginx_access.log" source: "logs/nginx_access_json.log" --- # Nginx Access Log - Find Largest Responses ## Prompt Analyze the Nginx JSON...
task_log_nginx_status_codes
Nginx Access Log - HTTP Status Code Distribution
log_analysis
hybrid
180
Analyze the Nginx JSON access log at `nginx_access.log` and produce a report on HTTP status code distribution. Each line is a JSON object with fields: `time`, `remote_ip`, `remote_user`, `request`, `response`, `bytes`, `referrer`, `agent`. Your report should include: 1. **Total Requests**: Total number of log entries...
The agent should parse all 1000 JSON log entries and produce: **Total Requests:** 1000 **Status Code Breakdown:** - 200: 35 (3.5%) - 206: 1 (0.1%) - 304: 274 (27.4%) - 403: 2 (0.2%) - 404: 688 (68.8%) **Status Code Categories:** - 2xx: 36 (3.6%) - 3xx: 274 (27.4%) - 4xx: 690 (69.0%) **Key observations:** - The log ...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Nginx status code distribution task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "status_code_report.md" if not report_file.exists(): return { ...
- [ ] `status_code_report.md` is created in the workspace - [ ] Total request count is reported (1000) - [ ] All observed status codes are listed with counts (200, 206, 304, 403, 404) - [ ] Status codes are grouped by category (2xx, 3xx, 4xx) - [ ] Top error-generating IPs are identified ---
{}
false
[]
[]
[{"dest": "nginx_access.log", "source": "logs/nginx_access_json.log"}]
["logs/nginx_access_json.log"]
H4sIAAAAAAAC/+1dS28dx3LWOr+C0OoGiUddXf30zoCN602AuzCQpUHZjKJEohRSRCwEyW/PDA8NSEqq53A4VV1TzQMLliiDVqlfVfU96t2HN7evrt+8vf7j18vffru6vf31324/XE/vPrx5sdvHzZ8Uwv2/58/X/84uZ8h/fu30dUDA8OLCvRD43N1+uryZ//cvxvz818tPb99fvfz+4iXkV/90+fmVdxC/d+V7F79Hf/EPy4K8/MeLlzdX7z98uvr17cflP604QXFThgm/+L2726ub5Xe/O33tP+6ubj8tv/7rT79cvPr9w39ev/tw...
--- id: task_log_nginx_status_codes name: Nginx Access Log - HTTP Status Code Distribution category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "nginx_access.log" source: "logs/nginx_access_json.log" --- # Nginx Access Log - HTTP Status Code Distribution ## Prompt Analyze t...
task_log_nginx_traffic
Nginx Access Log - Traffic Patterns by Time
log_analysis
hybrid
180
Analyze the Nginx JSON access log at `nginx_access.log` and produce a report on traffic patterns over time. Each line is a JSON object with fields: `time`, `remote_ip`, `remote_user`, `request`, `response`, `bytes`, `referrer`, `agent`. Your report should include: 1. **Time Range**: The full date/time range covered b...
The agent should parse all 1000 JSON log entries and produce: **Time Range:** May 17, 2015, 08:05:01 to 16:05:10 UTC (approximately 8 hours) **Hourly Breakdown (approximate):** - 08:xx — the log starts mid-hour - Traffic is distributed across the 8-hour window - The log contains entries timestamped between 08:05 and ...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Nginx traffic patterns task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "traffic_report.md" if not report_file.exists(): return { "output...
- [ ] `traffic_report.md` is created in the workspace - [ ] Time range is identified (May 17, 2015; approximately 08:05–16:05 UTC) - [ ] Traffic is broken down by time period (hourly or similar) - [ ] Peak/busiest periods are identified - [ ] Bandwidth or bytes transferred is analyzed ---
{}
false
[]
[]
[{"dest": "nginx_access.log", "source": "logs/nginx_access_json.log"}]
["logs/nginx_access_json.log"]
H4sIAAAAAAAC/+1dS28dx3LWOr+C0OoGiUddXf30zoCN602AuzCQpUHZjKJEohRSRCwEyW/PDA8NSEqq53A4VV1TzQMLliiDVqlfVfU96t2HN7evrt+8vf7j18vffru6vf31324/XE/vPrx5sdvHzZ8Uwv2/58/X/84uZ8h/fu30dUDA8OLCvRD43N1+uryZ//cvxvz818tPb99fvfz+4iXkV/90+fmVdxC/d+V7F79Hf/EPy4K8/MeLlzdX7z98uvr17cflP604QXFThgm/+L2726ub5Xe/O33tP+6ubj8tv/7rT79cvPr9w39ev/tw...
--- id: task_log_nginx_traffic name: Nginx Access Log - Traffic Patterns by Time category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "nginx_access.log" source: "logs/nginx_access_json.log" --- # Nginx Access Log - Traffic Patterns by Time ## Prompt Analyze the Nginx JSON a...
task_log_nginx_user_agents
Nginx Access Log - User Agent Analysis
log_analysis
hybrid
180
Analyze the Nginx JSON access log at `nginx_access.log` and produce a comprehensive user agent analysis. Each line is a JSON object with fields: `time`, `remote_ip`, `remote_user`, `request`, `response`, `bytes`, `referrer`, `agent`. Your report should include: 1. **Unique User Agents**: Total count of distinct user ...
The agent should parse all 1000 JSON log entries and produce: **Unique User Agents:** 14 distinct agent strings (including "-" for empty) **Top User Agents:** - `Debian APT-HTTP/1.3 (0.9.7.9)` — 370 requests (37.0%) - `Debian APT-HTTP/1.3 (0.8.16~exp12ubuntu10.16)` — 177 (17.7%) - `Debian APT-HTTP/1.3 (0.8.16~exp12ub...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Nginx user agent analysis task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "user_agent_report.md" if not report_file.exists(): return { "...
- [ ] `user_agent_report.md` is created in the workspace - [ ] All user agents are listed with counts - [ ] Agents are classified by type (package manager, bot, etc.) - [ ] The dominant agent (Debian APT) is identified as the primary client - [ ] Server purpose is correctly inferred (package repository/download mirror)...
{}
false
[]
[]
[{"dest": "nginx_access.log", "source": "logs/nginx_access_json.log"}]
["logs/nginx_access_json.log"]
H4sIAAAAAAAC/+1dS28dx3LWOr+C0OoGiUddXf30zoCN602AuzCQpUHZjKJEohRSRCwEyW/PDA8NSEqq53A4VV1TzQMLliiDVqlfVfU96t2HN7evrt+8vf7j18vffru6vf31324/XE/vPrx5sdvHzZ8Uwv2/58/X/84uZ8h/fu30dUDA8OLCvRD43N1+uryZ//cvxvz818tPb99fvfz+4iXkV/90+fmVdxC/d+V7F79Hf/EPy4K8/MeLlzdX7z98uvr17cflP604QXFThgm/+L2726ub5Xe/O33tP+6ubj8tv/7rT79cvPr9w39ev/tw...
--- id: task_log_nginx_user_agents name: Nginx Access Log - User Agent Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "nginx_access.log" source: "logs/nginx_access_json.log" --- # Nginx Access Log - User Agent Analysis ## Prompt Analyze the Nginx JSON access ...
task_log_ssh_brute_force
SSH Auth Log - Brute Force Detection
log_analysis
hybrid
180
You are a security analyst reviewing the OpenSSH authentication log at `auth.log`. Your job is to detect brute-force attack patterns and produce a threat assessment. Define a brute-force attack as: **more than 10 failed authentication attempts from a single IP address within the log period**. Your report should inclu...
The agent should identify these brute-force sources: **Primary Attackers:** - **183.62.140.253** — ~307 entries, heaviest attacker, likely dictionary attack - **187.141.143.180** — ~189 entries, sustained attack - **103.99.0.122** — ~83 entries - **112.95.230.3** — ~54 entries - **5.188.10.180** — ~30 entries - **185....
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the SSH brute force detection task.""" from pathlib import Path import json scores = {} workspace = Path(workspace_path) report_file = workspace / "brute_force_report.json" if not report_file.exists(): ret...
- [ ] `brute_force_report.json` is created in the workspace - [ ] At least 3 brute-force source IPs are identified - [ ] 183.62.140.253 is identified as the top attacker - [ ] Attack type (dictionary vs targeted) is classified for each source - [ ] Recommendations for countermeasures are provided ---
{}
false
[]
[]
[{"dest": "auth.log", "source": "logs/openssh_auth.log"}]
["logs/openssh_auth.log"]
H4sIAAAAAAAC/+19XXMjx5Wlnzdi/wPmbfxAbOV3Jic0EbKtidCO7VFYelqHwoEm0S3aTYBDgpJbv34LKIBN5D1ZyMq6xcqNbdiyOtrkPQcnz82vupn1cfvh6X9tH9abp6ef/rZ63v20/Lj98BveT9N+rNaHf7ef838b0WgtT3/X/b2QQsrfLJrfvMHn+Wm3emzhf/P/5+cP65uFaBaNvTbmWtvFH1fvvv8/i9YMt3+VWjbNj9eLx/XP68en9eJ+9fBwt/mwuPlpffOP/R8+rHer29vHu8377eL99nGxeVrerx4fP60+3v7j/erm15tf...
--- id: task_log_ssh_brute_force name: SSH Auth Log - Brute Force Detection category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "auth.log" source: "logs/openssh_auth.log" --- # SSH Auth Log - Brute Force Detection ## Prompt You are a security analyst reviewing the OpenSSH ...
task_log_ssh_failed_logins
SSH Auth Log - Failed Login Analysis
log_analysis
hybrid
180
Analyze the OpenSSH authentication log at `auth.log` and produce a detailed report on failed login attempts. The log is from a server named "LabSZ" and covers SSH authentication events. Your report should include: 1. **Overview**: Total log entries, date range, total failed login attempts 2. **Failed Password Attempt...
The agent should parse the 1500 log entries and produce: **Overview:** - 1500 log entries - Date: December 10 (times range from ~06:55 to ~10:59) - ~366 "Failed password" entries - ~100 "Invalid user" entries **Top Attacking IPs:** - 183.62.140.253 — ~307 entries (dominant attacker) - 187.141.143.180 — ~189 entries -...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the SSH failed login analysis task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "failed_login_report.md" if not report_file.exists(): return { ...
- [ ] `failed_login_report.md` is created in the workspace - [ ] Total failed attempts are counted (approximately 366 failed passwords) - [ ] Top attacking IPs are identified (183.62.140.253 as the top attacker) - [ ] Invalid usernames are listed (admin, oracle, support as top targets) - [ ] The server is assessed as b...
{}
false
[]
[]
[{"dest": "auth.log", "source": "logs/openssh_auth.log"}]
["logs/openssh_auth.log"]
H4sIAAAAAAAC/+19XXMjx5Wlnzdi/wPmbfxAbOV3Jic0EbKtidCO7VFYelqHwoEm0S3aTYBDgpJbv34LKIBN5D1ZyMq6xcqNbdiyOtrkPQcnz82vupn1cfvh6X9tH9abp6ef/rZ63v20/Lj98BveT9N+rNaHf7ef838b0WgtT3/X/b2QQsrfLJrfvMHn+Wm3emzhf/P/5+cP65uFaBaNvTbmWtvFH1fvvv8/i9YMt3+VWjbNj9eLx/XP68en9eJ+9fBwt/mwuPlpffOP/R8+rHer29vHu8377eL99nGxeVrerx4fP60+3v7j/erm15tf...
--- id: task_log_ssh_failed_logins name: SSH Auth Log - Failed Login Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "auth.log" source: "logs/openssh_auth.log" --- # SSH Auth Log - Failed Login Analysis ## Prompt Analyze the OpenSSH authentication log at `auth...
task_log_ssh_successful
SSH Auth Log - Successful Authentication Summary
log_analysis
hybrid
180
Analyze the OpenSSH authentication log at `auth.log` and produce a report focused on successful authentications. Among the noise of failed attempts, identify all legitimate access. Your report should include: 1. **Successful Logins**: List every successful authentication with timestamp, username, source IP, port, and...
The agent should identify: **Successful Logins:** - Only 1 successful login in the entire log: - Time: Dec 10 09:32:20 - User: fztu - Source: 119.137.62.142 - Port: 49116 - Method: password (ssh2) - Entry: "Accepted password for fztu from 119.137.62.142 port 49116 ssh2" **Success vs Failure Ratio:** - 1 s...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the SSH successful authentication summary task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "successful_auth_report.md" if not report_file.exists(): retur...
- [ ] `successful_auth_report.md` is created in the workspace - [ ] The single successful login is identified (user fztu, IP 119.137.62.142) - [ ] Success/failure ratio is calculated (1 success vs hundreds of failures) - [ ] The successful login IP is checked against failed attempt sources - [ ] An assessment of whethe...
{}
false
[]
[]
[{"dest": "auth.log", "source": "logs/openssh_auth.log"}]
["logs/openssh_auth.log"]
H4sIAAAAAAAC/+19XXMjx5Wlnzdi/wPmbfxAbOV3Jic0EbKtidCO7VFYelqHwoEm0S3aTYBDgpJbv34LKIBN5D1ZyMq6xcqNbdiyOtrkPQcnz82vupn1cfvh6X9tH9abp6ef/rZ63v20/Lj98BveT9N+rNaHf7ef838b0WgtT3/X/b2QQsrfLJrfvMHn+Wm3emzhf/P/5+cP65uFaBaNvTbmWtvFH1fvvv8/i9YMt3+VWjbNj9eLx/XP68en9eJ+9fBwt/mwuPlpffOP/R8+rHer29vHu8377eL99nGxeVrerx4fP60+3v7j/erm15tf...
--- id: task_log_ssh_successful name: SSH Auth Log - Successful Authentication Summary category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "auth.log" source: "logs/openssh_auth.log" --- # SSH Auth Log - Successful Authentication Summary ## Prompt Analyze the OpenSSH authen...
task_log_ssh_unusual_times
SSH Auth Log - Unusual Hour Login Detection
log_analysis
hybrid
180
Analyze the OpenSSH authentication log at `auth.log` and identify login activity occurring at unusual hours. Assume normal business hours are 08:00–18:00 local server time. Your report should include: 1. **Hourly Distribution**: Count of authentication events per hour 2. **Off-Hours Activity**: All authentication eve...
The agent should parse the log and produce: **Hourly Distribution:** - 06:xx — 7 entries (log starts at 06:55) - 07:xx — 169 entries - 08:xx — 118 entries - 09:xx — 676 entries (peak hour) - 10:xx — 530 entries (log ends at 10:59) **Off-Hours Activity:** - 7 entries before 07:00 (at 06:55–06:56) - All pre-08:00 entri...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the SSH unusual hours detection task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "unusual_hours_report.md" if not report_file.exists(): return { ...
- [ ] `unusual_hours_report.md` is created in the workspace - [ ] Hourly distribution of events is provided - [ ] Off-hours (pre-08:00) events are separately identified - [ ] The successful login timing is noted (09:32:20, during business hours) - [ ] Attack timing patterns are analyzed ---
{}
false
[]
[]
[{"dest": "auth.log", "source": "logs/openssh_auth.log"}]
["logs/openssh_auth.log"]
H4sIAAAAAAAC/+19XXMjx5Wlnzdi/wPmbfxAbOV3Jic0EbKtidCO7VFYelqHwoEm0S3aTYBDgpJbv34LKIBN5D1ZyMq6xcqNbdiyOtrkPQcnz82vupn1cfvh6X9tH9abp6ef/rZ63v20/Lj98BveT9N+rNaHf7ef838b0WgtT3/X/b2QQsrfLJrfvMHn+Wm3emzhf/P/5+cP65uFaBaNvTbmWtvFH1fvvv8/i9YMt3+VWjbNj9eLx/XP68en9eJ+9fBwt/mwuPlpffOP/R8+rHer29vHu8377eL99nGxeVrerx4fP60+3v7j/erm15tf...
--- id: task_log_ssh_unusual_times name: SSH Auth Log - Unusual Hour Login Detection category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "auth.log" source: "logs/openssh_auth.log" --- # SSH Auth Log - Unusual Hour Login Detection ## Prompt Analyze the OpenSSH authenticatio...
task_log_ssh_user_activity
SSH Auth Log - User Login Activity Report
log_analysis
hybrid
180
Analyze the OpenSSH authentication log at `auth.log` and produce a user-focused activity report. For every username mentioned in the log (both valid and invalid), summarize their authentication activity. Your report should include: 1. **All Usernames Attempted**: List every username that appears in the log (both vali...
The agent should identify: **Invalid Users (top by frequency):** - admin (18 attempts), oracle (6), support (5), test (4), inspur (3), 0 (3), matlab (3), webmaster (2), guest (2), 1234 (2), and others **Valid Users:** - fztu — the only user with a successful login - root — likely a valid user that's targeted (check f...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the SSH user activity report task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "user_activity_report.md" if not report_file.exists(): return { ...
- [ ] `user_activity_report.md` is created in the workspace - [ ] Both valid and invalid usernames are listed - [ ] The most-targeted username (admin) is identified - [ ] Username patterns are analyzed (dictionary attack, common defaults) - [ ] A risk assessment is provided for the most dangerous usernames ---
{}
false
[]
[]
[{"dest": "auth.log", "source": "logs/openssh_auth.log"}]
["logs/openssh_auth.log"]
H4sIAAAAAAAC/+19XXMjx5Wlnzdi/wPmbfxAbOV3Jic0EbKtidCO7VFYelqHwoEm0S3aTYBDgpJbv34LKIBN5D1ZyMq6xcqNbdiyOtrkPQcnz82vupn1cfvh6X9tH9abp6ef/rZ63v20/Lj98BveT9N+rNaHf7ef838b0WgtT3/X/b2QQsrfLJrfvMHn+Wm3emzhf/P/5+cP65uFaBaNvTbmWtvFH1fvvv8/i9YMt3+VWjbNj9eLx/XP68en9eJ+9fBwt/mwuPlpffOP/R8+rHer29vHu8377eL99nGxeVrerx4fP60+3v7j/erm15tf...
--- id: task_log_ssh_user_activity name: SSH Auth Log - User Login Activity Report category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "auth.log" source: "logs/openssh_auth.log" --- # SSH Auth Log - User Login Activity Report ## Prompt Analyze the OpenSSH authentication lo...
task_log_syslog_anomalies
Linux Syslog - Anomaly Detection
log_analysis
hybrid
180
Analyze the Linux syslog at `syslog.log` and identify anomalous or suspicious entries. The log is from a server named "combo" running a Linux 2.6 kernel, covering several months of activity. Your report should include: 1. **Log Overview**: Total entries, date range, top services by volume 2. **Security Anomalies**: E...
The agent should parse 5000 entries and identify: **Log Overview:** - 5000 entries, June 9 to September 14 (2005, based on kernel version) - Top services: ftpd (1655), sshd/pam_unix (1610), kernel (545), su/pam_unix (394) **Security Anomalies:** 1. **rpc.statd format string attack** (~9 entries on Jun 13): - `geth...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Linux syslog anomaly detection task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "syslog_anomalies.md" if not report_file.exists(): return { ...
- [ ] `syslog_anomalies.md` is created in the workspace - [ ] Log overview with date range and service breakdown is provided - [ ] rpc.statd format string attack is identified as a security anomaly - [ ] FTP connection patterns are analyzed - [ ] SSH authentication failures are flagged ---
{}
false
[]
[]
[{"dest": "syslog.log", "source": "logs/linux_syslog.log"}]
["logs/linux_syslog.log"]
H4sIAAAAAAAC/+y965bbOJYu2L/nKbC6T02G61g07hfV5Kxy2s7MOOmwfTKcVdWdXZ2jkBgROqGQVCJlO+rl+rH6Zw9ASpREESIoASLXKmdacVEI3/42sLGxcSH2ZHaXvJiMp8svvyVPyWR2F+nXP/n9D+r/BGPZd/3fznckBMEQrd/L30cYCfRPAP7TGf5bJulgocX/0z/mf/9rOQVAAcj7+h+GYDh7vJmB3BRGAEU0Qn2wiE0lpdH/dejT/aJU9uHlHCTL4TCOR/GoptxDo1IP8WIaT9alMobPgf4ZJLPlYhiDb8GL+WI2fPHwmNzl...
--- id: task_log_syslog_anomalies name: Linux Syslog - Anomaly Detection category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "syslog.log" source: "logs/linux_syslog.log" --- # Linux Syslog - Anomaly Detection ## Prompt Analyze the Linux syslog at `syslog.log` and identify ...
task_log_syslog_auth_failures
Linux Syslog - Authentication Failure Summary
log_analysis
hybrid
180
Analyze the Linux syslog at `syslog.log` and produce a comprehensive summary of all authentication failures. The log contains PAM authentication events from multiple services. Your report should include: 1. **Total Auth Failures**: Count all authentication failure entries across all services 2. **Failures by Service*...
The agent should parse the ~2000+ PAM-related entries and produce: **Total Auth Failures:** - Over 2000 authentication-related PAM entries - Primary sources: sshd(pam_unix) (~1610 entries), ftpd connections (~1655 entries) **Failures by Service:** - sshd(pam_unix) — the dominant source of auth failure messages - ftpd...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Linux syslog authentication failure summary task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "auth_failures_report.md" if not report_file.exists(): r...
- [ ] `auth_failures_report.md` is created in the workspace - [ ] Authentication failures are counted (2000+ pam-related entries) - [ ] Failures are broken down by service (sshd, ftpd, su, etc.) - [ ] Top source hosts are listed - [ ] Recommendations for security improvement are provided ---
{}
false
[]
[]
[{"dest": "syslog.log", "source": "logs/linux_syslog.log"}]
["logs/linux_syslog.log"]
H4sIAAAAAAAC/+y965bbOJYu2L/nKbC6T02G61g07hfV5Kxy2s7MOOmwfTKcVdWdXZ2jkBgROqGQVCJlO+rl+rH6Zw9ASpREESIoASLXKmdacVEI3/42sLGxcSH2ZHaXvJiMp8svvyVPyWR2F+nXP/n9D+r/BGPZd/3fznckBMEQrd/L30cYCfRPAP7TGf5bJulgocX/0z/mf/9rOQVAAcj7+h+GYDh7vJmB3BRGAEU0Qn2wiE0lpdH/dejT/aJU9uHlHCTL4TCOR/GoptxDo1IP8WIaT9alMobPgf4ZJLPlYhiDb8GL+WI2fPHwmNzl...
--- id: task_log_syslog_auth_failures name: Linux Syslog - Authentication Failure Summary category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "syslog.log" source: "logs/linux_syslog.log" --- # Linux Syslog - Authentication Failure Summary ## Prompt Analyze the Linux syslog...
task_log_syslog_boot
Linux Syslog Boot Sequence Analysis
log_analysis
hybrid
180
Analyze the Linux syslog file at `linux_syslog.log` and produce a boot sequence report. This log contains multiple boot cycles from a production server. Focus on the **first boot** recorded in the log and answer the following: 1. **System identification**: What is the kernel version, CPU model, and total available RAM...
The agent should parse the syslog file, identify the first boot sequence starting at `Jun 9 06:06:20`, and extract hardware and service information from kernel messages and daemon startup lines. **Key expected values from the first boot (Jun 9):** - **Kernel version**: `2.6.5-1.358` - **CPU**: Intel Pentium III (Cop...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the syslog boot analysis task. Expects boot_report.md in the workspace containing structured analysis of the Linux syslog boot sequence. """ from pathlib import Path scores = {} workspace = Path(workspace_pat...
- [ ] `boot_report.md` is created in the workspace - [ ] Kernel version `2.6.5-1.358` is identified - [ ] CPU is identified as Intel Pentium III (Coppermine) - [ ] RAM is reported as approximately 126MB - [ ] Hard drive is identified as IBM-DTLA-307015 - [ ] Root filesystem is identified as EXT3 - [ ] 3Com 3c905C Torna...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "logs/linux_syslog.log", "dest": "linux_syslog.log"}]
["logs/linux_syslog.log"]
H4sIAAAAAAAC/+y965bbOJYu2L/nKbC6T02G61g07hfV5Kxy2s7MOOmwfTKcVdWdXZ2jkBgROqGQVCJlO+rl+rH6Zw9ASpREESIoASLXKmdacVEI3/42sLGxcSH2ZHaXvJiMp8svvyVPyWR2F+nXP/n9D+r/BGPZd/3fznckBMEQrd/L30cYCfRPAP7TGf5bJulgocX/0z/mf/9rOQVAAcj7+h+GYDh7vJmB3BRGAEU0Qn2wiE0lpdH/dejT/aJU9uHlHCTL4TCOR/GoptxDo1IP8WIaT9alMobPgf4ZJLPlYhiDb8GL+WI2fPHwmNzl...
--- id: task_log_syslog_boot name: Linux Syslog Boot Sequence Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: logs/linux_syslog.log dest: linux_syslog.log --- ## Prompt Analyze the Linux syslog file at `linux...
task_log_syslog_cron
Linux Syslog - Cron Job Execution Analysis
log_analysis
hybrid
180
Analyze the Linux syslog at `syslog.log` and produce a report on cron job and scheduled task activity. Look for crond, anacron, logrotate, and any other scheduled execution evidence. Your report should include: 1. **Cron Service Status**: When does crond start? How many startup events are there? 2. **Anacron Activity...
The agent should identify: **Cron/Anacron Startups:** - crond startup: Jun 9 06:06:49, Jun 10 11:32:10, Jul 27 14:42:23 (3 events, aligned with system boots) - anacron startup: Jun 9 06:06:51, Jun 10 11:32:12, Jul 27 14:42:25 (follows crond immediately) **Logrotate Activity:** - 97 logrotate entries in the log - Logr...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Linux syslog cron job analysis task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "cron_analysis.md" if not report_file.exists(): return { ...
- [ ] `cron_analysis.md` is created in the workspace - [ ] Crond and anacron startup events are documented - [ ] Logrotate activity is identified (97 entries) - [ ] su(pam_unix) sessions are linked to scheduled tasks - [ ] Recurring patterns are identified (daily, at boot, etc.) ---
{}
false
[]
[]
[{"dest": "syslog.log", "source": "logs/linux_syslog.log"}]
["logs/linux_syslog.log"]
H4sIAAAAAAAC/+y965bbOJYu2L/nKbC6T02G61g07hfV5Kxy2s7MOOmwfTKcVdWdXZ2jkBgROqGQVCJlO+rl+rH6Zw9ASpREESIoASLXKmdacVEI3/42sLGxcSH2ZHaXvJiMp8svvyVPyWR2F+nXP/n9D+r/BGPZd/3fznckBMEQrd/L30cYCfRPAP7TGf5bJulgocX/0z/mf/9rOQVAAcj7+h+GYDh7vJmB3BRGAEU0Qn2wiE0lpdH/dejT/aJU9uHlHCTL4TCOR/GoptxDo1IP8WIaT9alMobPgf4ZJLPlYhiDb8GL+WI2fPHwmNzl...
--- id: task_log_syslog_cron name: Linux Syslog - Cron Job Execution Analysis category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "syslog.log" source: "logs/linux_syslog.log" --- # Linux Syslog - Cron Job Execution Analysis ## Prompt Analyze the Linux syslog at `syslog.log...
task_log_syslog_services
Linux Syslog - Service Start/Stop Summary
log_analysis
hybrid
180
Analyze the Linux syslog at `syslog.log` and produce a summary of all service start and stop events. The log is from a server named "combo" running Linux 2.6. Your report should include: 1. **Service Inventory**: List every service mentioned in the log with startup or shutdown events 2. **System Boot Events**: Identi...
The agent should identify: **System Boots (3 detected):** 1. Jun 9 ~06:06 — Full boot (syslogd, klogd, kernel, irqbalance, portmap, etc.) 2. Jun 10 ~11:32 — Full boot (same service sequence) 3. Jul 27 ~14:42 — Another boot event **Service Inventory (20+ services with startup events):** - Core: syslogd, klogd, irqbala...
```python def grade(transcript: list, workspace_path: str) -> dict: """Grade the Linux syslog service status summary task.""" from pathlib import Path scores = {} workspace = Path(workspace_path) report_file = workspace / "service_status_report.md" if not report_file.exists(): return {...
- [ ] `service_status_report.md` is created in the workspace - [ ] System boot events are identified (at least 2 boots found) - [ ] Services are listed with their start/stop events - [ ] CUPS frequent restarts are noted (17 startups) - [ ] Service startup ordering is analyzed ---
{}
false
[]
[]
[{"dest": "syslog.log", "source": "logs/linux_syslog.log"}]
["logs/linux_syslog.log"]
H4sIAAAAAAAC/+y965bbOJYu2L/nKbC6T02G61g07hfV5Kxy2s7MOOmwfTKcVdWdXZ2jkBgROqGQVCJlO+rl+rH6Zw9ASpREESIoASLXKmdacVEI3/42sLGxcSH2ZHaXvJiMp8svvyVPyWR2F+nXP/n9D+r/BGPZd/3fznckBMEQrd/L30cYCfRPAP7TGf5bJulgocX/0z/mf/9rOQVAAcj7+h+GYDh7vJmB3BRGAEU0Qn2wiE0lpdH/dejT/aJU9uHlHCTL4TCOR/GoptxDo1IP8WIaT9alMobPgf4ZJLPlYhiDb8GL+WI2fPHwmNzl...
--- id: task_log_syslog_services name: Linux Syslog - Service Start/Stop Summary category: log_analysis grading_type: hybrid timeout_seconds: 180 workspace_files: - dest: "syslog.log" source: "logs/linux_syslog.log" --- # Linux Syslog - Service Start/Stop Summary ## Prompt Analyze the Linux syslog at `syslog.l...
task_market_research
Competitive Market Research
research
hybrid
300
Create a competitive landscape analysis for the **enterprise observability and APM (Application Performance Monitoring)** market segment. Based on your knowledge, identify the top 5 players, their key differentiators, market trends, and typical pricing models. **IMPORTANT: You MUST save your report to a file named exa...
The agent should: 1. Identify the major competitors (e.g., Datadog, New Relic, Dynatrace, Splunk, Grafana Labs, Elastic, etc.) 2. For each competitor, document: - Company overview and market position - Key product differentiators - Typical pricing model (per-host, per-GB, per-user, etc.) - Notable strength...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the market research task based on file creation and structural content. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: ...
### Criterion 1: Research Depth and Accuracy (Weight: 30%) **Score 1.0**: Report contains specific, accurate information about each competitor. Details go beyond surface-level descriptions — includes concrete product names, specific features, and market positioning. If web search was used, information is current. If b...
- [ ] File `market_research.md` created - [ ] Report identifies at least 5 competitors - [ ] Each competitor has a meaningful profile (not just a name) - [ ] Report includes a comparison table or matrix - [ ] Report covers pricing models for competitors - [ ] Report discusses current market trends - [ ] Information app...
{}
false
[]
[]
[]
[]
--- id: task_market_research name: Competitive Market Research category: research grading_type: hybrid timeout_seconds: 300 workspace_files: [] --- ## Prompt Create a competitive landscape analysis for the **enterprise observability and APM (Application Performance Monitoring)** market segment. Based on your knowledg...
task_meeting_advisory_acronyms
NTIA Advisory Board Acronym Glossary
meeting_analysis
hybrid
180
I have a transcript of a government advisory committee meeting in `meeting-transcript.md`. This is a meeting of the Commerce Spectrum Management Advisory Committee (CSMAC) held on May 30, 2012, discussing federal spectrum management and sharing. Please analyze the transcript and build a comprehensive acronym glossary ...
The agent should: 1. Read and parse the meeting transcript 2. Identify all acronyms and abbreviations 3. Determine their full forms (from context or domain knowledge) 4. Categorize and sort them Key acronyms expected (minimum set): | Acronym | Full Form | Category | |---------|-----------|----------| | CSMAC | Comme...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the acronym glossary task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to scores (0...
### Criterion 1: Acronym Coverage (Weight: 35%) **Score 1.0**: 25+ acronyms identified, including obscure ones like CSEA, ISART, TSB, CMRS, and contextual uses like NFL (metaphorical) and TMI. Both obvious government acronyms and technical spectrum terms are covered. **Score 0.75**: 20-24 acronyms identified, covering...
- [ ] File `acronym_glossary.md` is created - [ ] CSMAC correctly expanded - [ ] NTIA correctly expanded - [ ] At least 15 unique acronyms identified - [ ] At least 20 unique acronyms identified (bonus threshold) - [ ] Technical acronyms included (MHz, GHz, LTE, UAV, STA) - [ ] Government agencies included (FCC, DoD, D...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "meetings/2012-05-30-meeting-transcript-ntia-csmac.md", "dest": "meeting-transcript.md"}]
["meetings/2012-05-30-meeting-transcript-ntia-csmac.md"]
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--- id: task_meeting_advisory_acronyms name: NTIA Advisory Board Acronym Glossary category: meeting_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: meetings/2012-05-30-meeting-transcript-ntia-csmac.md dest: meeting-transcript.md --- ...
task_meeting_advisory_attendees
NTIA Advisory Board Attendee List
meeting_analysis
hybrid
180
I have a transcript of a government advisory committee meeting in `meeting-transcript.md`. This is a meeting of the Commerce Spectrum Management Advisory Committee (CSMAC) held on May 30, 2012. Please analyze the transcript and create a structured attendee list in a file called `attendees.md`. For each attendee, inclu...
The agent should: 1. Read and parse the meeting transcript 2. Identify all named individuals from the "Members Present" lists, "Also Present" section, and dialogue 3. Determine attendance mode from the asterisk notation (phone) and roll call 4. Categorize each person's role and level of participation 5. Produce a well...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the meeting attendee list task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to scor...
### Criterion 1: Completeness of Attendee Identification (Weight: 35%) **Score 1.0**: All committee members, officials, and public participants are identified with correct names and roles. No one is missed. **Score 0.75**: Most attendees identified (18+) with only minor omissions. **Score 0.5**: Majority identified bu...
- [ ] File `attendees.md` is created - [ ] Brian Fontes correctly identified as Chair with NENA affiliation - [ ] At least 15 committee members identified by name - [ ] Remote/phone attendees correctly identified (Hatfield, Feldman, McGinnis, Stancil, Reaser, Donovan) - [ ] Non-member officials listed (Strickling, Nebb...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "meetings/2012-05-30-meeting-transcript-ntia-csmac.md", "dest": "meeting-transcript.md"}]
["meetings/2012-05-30-meeting-transcript-ntia-csmac.md"]
H4sIAAAAAAAC/+293ZIb15Ul7Ot6iozwRNPuQLFJybLd9gWDpCip1KKkYJXNUM/MRQI4KKSZyIQzEwVBHf0uczvPMS82e+2/s08CpGfm4rv5qqLbVBUSmSfPz/5de+1dSlPT3Y//8tmz559dP/vi+vNn1zv52/U01N24Gpr9dN1NTX29Gnf16ulu/av/y59n9PP73/2O/6Wf8t/Pnn/+2e+e29/k789//4fff/Gr6tmv/j/4OYxTPdDjf/X/z59fV2/rU/X5s0WFDVC9laWv7nzpr66+PXSJPpUrrq7+8v3N3Zsvq9u7l3dvbqsfvqpe...
--- id: task_meeting_advisory_attendees name: NTIA Advisory Board Attendee List category: meeting_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: meetings/2012-05-30-meeting-transcript-ntia-csmac.md dest: meeting-transcript.md --- #...
task_meeting_advisory_stakeholders
NTIA Advisory Board Stakeholder Interests
meeting_analysis
hybrid
180
I have a transcript of a government advisory committee meeting in `meeting-transcript.md`. This is a meeting of the Commerce Spectrum Management Advisory Committee (CSMAC) held on May 30, 2012, focused on sharing federal spectrum (1755-1850 MHz band) between government and commercial wireless broadband. Please analyze...
The agent should: 1. Read and analyze the meeting transcript 2. Identify the major stakeholder groups and their positions 3. Map individual members to their represented interests 4. Capture the nuanced positions on sharing vs relocation Key stakeholder groups and their interests: - **NTIA/Karl Nebbia**: Favors shari...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the stakeholder analysis task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to score...
### Criterion 1: Stakeholder Identification Completeness (Weight: 30%) **Score 1.0**: All major stakeholder groups identified (government agencies, commercial carriers, equipment makers, defense contractors, academics, public interest, public participants) with specific named representatives where applicable. **Score ...
- [ ] File `stakeholder_analysis.md` is created - [ ] Government stakeholders identified (NTIA, DoD, DHS/Justice, White House/OSTP) - [ ] Commercial industry stakeholders identified (carriers, equipment manufacturers) - [ ] NTIA's preference for sharing over pure relocation noted - [ ] $18 billion relocation cost menti...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "meetings/2012-05-30-meeting-transcript-ntia-csmac.md", "dest": "meeting-transcript.md"}]
["meetings/2012-05-30-meeting-transcript-ntia-csmac.md"]
H4sIAAAAAAAC/+293ZIb15Ul7Ot6iozwRNPuQLFJybLd9gWDpCip1KKkYJXNUM/MRQI4KKSZyIQzEwVBHf0uczvPMS82e+2/s08CpGfm4rv5qqLbVBUSmSfPz/5de+1dSlPT3Y//8tmz559dP/vi+vNn1zv52/U01N24Gpr9dN1NTX29Gnf16ulu/av/y59n9PP73/2O/6Wf8t/Pnn/+2e+e29/k789//4fff/Gr6tmv/j/4OYxTPdDjf/X/z59fV2/rU/X5s0WFDVC9laWv7nzpr66+PXSJPpUrrq7+8v3N3Zsvq9u7l3dvbqsfvqpe...
--- id: task_meeting_advisory_stakeholders name: NTIA Advisory Board Stakeholder Interests category: meeting_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: meetings/2012-05-30-meeting-transcript-ntia-csmac.md dest: meeting-transcrip...
task_meeting_advisory_technical
NTIA Advisory Board Technical Discussions
meeting_analysis
hybrid
180
I have a transcript of a government advisory committee meeting in `meeting-transcript.md`. This is a meeting of the Commerce Spectrum Management Advisory Committee (CSMAC) held on May 30, 2012, focused on federal spectrum management in the 1755-1850 MHz band. Please analyze the transcript and extract all technical dis...
The agent should: 1. Read and parse the meeting transcript 2. Extract all technical topics discussed 3. Map topics to the five proposed working groups 4. Capture the technical parameter debate Key technical topics expected: - **Weather satellite receivers (1695-1710 MHz)**: Exclusion areas around receivers, potentia...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the technical discussions extraction task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion na...
### Criterion 1: Technical Accuracy (Weight: 35%) **Score 1.0**: All technical topics accurately extracted with correct frequency bands, system descriptions, and interference dynamics. The direction of interference (e.g., satellite uplinks—interference is into industry, not from) is correctly noted. **Score 0.75**: Mo...
- [ ] File `technical_discussions.md` is created - [ ] Weather satellite exclusion areas topic covered (1695-1710 MHz) - [ ] Law enforcement surveillance transition plan described (three-step process) - [ ] Satellite uplink protection issue identified (interference direction noted) - [ ] Electronic warfare training nee...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "meetings/2012-05-30-meeting-transcript-ntia-csmac.md", "dest": "meeting-transcript.md"}]
["meetings/2012-05-30-meeting-transcript-ntia-csmac.md"]
H4sIAAAAAAAC/+293ZIb15Ul7Ot6iozwRNPuQLFJybLd9gWDpCip1KKkYJXNUM/MRQI4KKSZyIQzEwVBHf0uczvPMS82e+2/s08CpGfm4rv5qqLbVBUSmSfPz/5de+1dSlPT3Y//8tmz559dP/vi+vNn1zv52/U01N24Gpr9dN1NTX29Gnf16ulu/av/y59n9PP73/2O/6Wf8t/Pnn/+2e+e29/k789//4fff/Gr6tmv/j/4OYxTPdDjf/X/z59fV2/rU/X5s0WFDVC9laWv7nzpr66+PXSJPpUrrq7+8v3N3Zsvq9u7l3dvbqsfvqpe...
--- id: task_meeting_advisory_technical name: NTIA Advisory Board Technical Discussions category: meeting_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: meetings/2012-05-30-meeting-transcript-ntia-csmac.md dest: meeting-transcript.m...
task_meeting_advisory_timeline
NTIA Advisory Board Timeline and Deadlines
meeting_analysis
hybrid
180
I have a transcript of a government advisory committee meeting in `meeting-transcript.md`. This is a meeting of the Commerce Spectrum Management Advisory Committee (CSMAC) held on May 30, 2012. Please analyze the transcript and extract all references to timelines, deadlines, schedules, and milestones into a structured...
The agent should: 1. Read and parse the meeting transcript 2. Extract all temporal references (specific dates, relative timeframes, durations) 3. Organize them chronologically 4. Distinguish between past events, current status, and future deadlines Key timeline entries expected: **Historical/Past:** - June 2010: Pre...
```python def grade(transcript: list, workspace_path: str) -> dict: """ Grade the timeline extraction task. Args: transcript: Parsed JSONL transcript as list of dicts workspace_path: Path to the task's isolated workspace directory Returns: Dict mapping criterion names to scores...
### Criterion 1: Completeness of Timeline Entries (Weight: 35%) **Score 1.0**: All major timeline entries captured — historical references (2010 memorandum, October 2011 report, prior 1710-1755 relocation, 5 GHz Wi-Fi precedent, 2001 cost estimates), current status items, and future deadlines (September, January, next...
- [ ] File `timeline.md` is created - [ ] June 2010 Presidential memorandum referenced - [ ] October 2011 report deadline mentioned - [ ] Next CSMAC meeting (July 24, Boulder, CO) noted - [ ] September 2012 target for weather satellite working group - [ ] January 2013 target for remaining working groups - [ ] 10-year t...
{"automated": 0.6, "llm_judge": 0.4}
false
[]
[]
[{"source": "meetings/2012-05-30-meeting-transcript-ntia-csmac.md", "dest": "meeting-transcript.md"}]
["meetings/2012-05-30-meeting-transcript-ntia-csmac.md"]
H4sIAAAAAAAC/+293ZIb15Ul7Ot6iozwRNPuQLFJybLd9gWDpCip1KKkYJXNUM/MRQI4KKSZyIQzEwVBHf0uczvPMS82e+2/s08CpGfm4rv5qqLbVBUSmSfPz/5de+1dSlPT3Y//8tmz559dP/vi+vNn1zv52/U01N24Gpr9dN1NTX29Gnf16ulu/av/y59n9PP73/2O/6Wf8t/Pnn/+2e+e29/k789//4fff/Gr6tmv/j/4OYxTPdDjf/X/z59fV2/rU/X5s0WFDVC9laWv7nzpr66+PXSJPpUrrq7+8v3N3Zsvq9u7l3dvbqsfvqpe...
--- id: task_meeting_advisory_timeline name: NTIA Advisory Board Timeline and Deadlines category: meeting_analysis grading_type: hybrid timeout_seconds: 180 grading_weights: automated: 0.6 llm_judge: 0.4 workspace_files: - source: meetings/2012-05-30-meeting-transcript-ntia-csmac.md dest: meeting-transcript.m...