dataset card: the README's story in the README's order (evalroute 0.8.3)
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README.md
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---
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license: mit
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pretty_name: evalroute flywheel
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configs:
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- config_name: measured
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data_files:
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- split: train
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path:
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- measured/*/*.jsonl
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- measured/*/*.csv
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- measured/models/*.json
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- config_name: routes
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data_files:
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- split: train
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path: routes/routes.yaml
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---
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license: mit
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pretty_name: evalroute flywheel
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configs:
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- config_name: measured
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data_files:
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- split: train
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path:
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- measured/*/*.jsonl
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- measured/*/*.csv
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- measured/models/*.json
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- config_name: routes
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data_files:
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- split: train
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path: routes/routes.yaml
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- config_name: contributed
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data_files:
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- split: train
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path: contributed/*/*.jsonl
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---
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# evalroute flywheel
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Honest size: three lanes are **measured** (n=10 each; arms tied at p=1.0) and
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six are **priors** from public benchmarks — not our runs. The pipe is real,
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the table is small, your outcomes grow it.
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Configs: **measured** — Tier-A harness artifacts (`runs.jsonl`, `report.csv`,
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`tasks.jsonl`, models manifests); written only by harness runs, never accepts
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contributed rows · **routes** — the `routes.yaml` that `evalroute sync` pins
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(`MANIFEST.json` says what produced it) · **contributed** — redacted outcome
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rows from opted-in installs under `contributed/<contributor>/`.
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## What a contributed row carries — only this, whitelist-redacted
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| key | what it is |
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| --- | --- |
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| `kind` | always `outcome` |
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| `route_lane`, `route_model`, `route_effort` | the routed arm |
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| `actual_model`, `actual_effort`, `arm_attribution` | the arm you actually ran (only when you confirmed it) |
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| `method`, `confidence` | how the route was classified |
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| `rated` | pass / fail / skip (rating corrections applied) |
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| `facets` | counts only, e.g. `{"long-doc": 1}` |
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| `week` | ISO year-week (`2026-W40`) — no timestamps |
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| `task_hash` | HMAC-SHA256 of the task text under a per-install salt |
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| `corrected`, `schema` | correction flag; schema version |
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Never leaves: task text, notes, paths, hostnames, session keys, the salt, the token.
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```bash
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uv tool install evalroute # or: pip install evalroute
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evalroute route --json "<one-line task>" # -> lane, model, effort, route_id
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# ... run the task on that arm, your own way ...
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evalroute rate pass --route-id <id> --model <model> --effort <effort> --note "why" --json
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evalroute report --json # what the ledger says so far
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evalroute contribute --dry-run # the exact rows that would go; grep, then drop the flag
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```
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Pooled rows are **observational**: they can contest a `priors` lane, never
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overwrite a `measured` one.
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- code + contract: https://github.com/keppy/evalroute (see its `AGENTS.md`)
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- Hermes plugin: https://github.com/keppy/hermes-plugin-evalroute
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