PinchBench (nearai-bench packaging)

A flat, self-contained repackaging of the upstream PinchBench skill suite (147 tasks, BENCHMARK_VERSION 2.0.0) for agent-harness consumption. Task content is unmodified — ids, filenames, prompts, rubrics and graders are byte-identical to upstream, so scores stay comparable to the pinchbench.com leaderboard.

Why this exists

Upstream ships tasks as a directory of markdown files plus a separate asset pool that has to be fetched with a shell script (and Git LFS). That is awkward for an eval/RL harness that wants to stream tasks across workers. Here, each task is one row: the verbatim markdown in task_md, and every asset the task references as a deterministic tar.gz in assets_tar. No clone, no LFS, no fetch script.

from datasets import load_dataset
ds = load_dataset("NEAR-AI/pinchbench", split="train")

Columns

Column	Type	Notes
task_id	string	Upstream task id, matches the upstream filename stem
name	string	Human-readable title
category	string	One of the 11 upstream categories
grading_type	string	automated | llm_judge | hybrid
timeout_seconds	int64	Upstream per-task budget
prompt	string	## Prompt section
expected_behavior	string	## Expected Behavior section
automated_checks	string	## Automated Checks — Python grade(transcript, workspace_path)
llm_judge_rubric	string	## LLM Judge Rubric, empty when the task has none
grading_criteria	string	## Grading Criteria; upstream judge falls back to this when the rubric is empty
grading_weights	string (JSON)	{"automated": w, "llm_judge": w}; {} ⇒ upstream 0.5/0.5 default
multi_session	bool	Multi-turn dialogue task
sessions	string (JSON)	Session definitions for multi-session tasks
prerequisites	string (JSON)	External tooling the task needs, e.g. ["cli:gh"]
workspace_files	string (JSON)	Upstream workspace_files verbatim — inline content entries and source/dest asset refs
asset_paths	string (JSON)	Asset paths bundled in assets_tar
assets_tar	string	base64(tar.gz) of this task's assets; "" when the task has none
task_md	string	The verbatim upstream markdown file, frontmatter included

Reconstructing an on-disk task

task_md + assets_tar reproduce the upstream layout byte-for-byte, which is what keeps grading identical:

import base64, io, json, tarfile, pathlib

def materialize(row, out_dir):
    out = pathlib.Path(out_dir); out.mkdir(parents=True, exist_ok=True)
    (out / f"{row['task_id']}.md").write_text(row["task_md"])
    if row["assets_tar"]:
        blob = base64.b64decode(row["assets_tar"])
        with tarfile.open(fileobj=io.BytesIO(blob), mode="r:gz") as tar:
            tar.extractall(out / "assets")
    return out

Then seed a task workspace from workspace_files: entries with path + content are written inline; entries with source + dest are copied from the extracted assets/ tree.

Grading

Upstream's three modes, unchanged:

• automated — run the Python grade() in automated_checks over the agent transcript and workspace; task score is the arithmetic mean of the returned criterion scores.
• llm_judge — score against llm_judge_rubric (or grading_criteria when the rubric is absent); the judge's total is the task score.
• hybrid — weighted combination using grading_weights, defaulting to 0.5/0.5.

A reference implementation of all three (a faithful port of upstream lib_grading.py) lives in nearai/benchmarks at src/adapters/pinchbench.rs.

Caveats

• 4 tasks (gws_*, gh_issue_triage) declare prerequisites — npm @juppytt/fws, the gh/gws CLIs — that no loader installs for you. They score near zero without that tooling (and without a mock backend for the live services they drive).
• Assets are scoped to what the 147 upstream tasks actually reference.

Provenance & license

• Upstream: https://github.com/pinchbench/skill — MIT, by Kilo Code (@olearycrew, @arpitg1991, @DJRHails, @evanjacobson, @iJaack).
• This repackaging: MIT, same terms. Task content unmodified; only the container format changed.
• Packaged by: NEAR AI for nearai-bench.