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skill-harness

v0.1.2

Published

Test/optimize loop for agent skills — run spec'd scenarios on pi, LLM-judge, score, review, re-run

Readme

skill-harness

A portable test / optimize loop for agent skills, driven from pi. Point it at a repo of skills, and for any skill with a tests/specification.yaml spec it will run each scenario, LLM-judge the transcript, score it against a ship bar, and open an interactive review UI so you can measure a SKILL.md edit.

Install

npm i -g skill-harness

The loop

  1. list — discover which skills have a spec.
  2. lint — validate specs/fixtures, free (no models, no keys) — the CI gate.
  3. run — run every scenario on pi (skill active), grade with an LLM judge, write results.yaml, print a scorecard.
  4. review — open an interactive matrix UI: flip verdicts, add notes, they persist back to results.yaml.
  5. add-test + re-run — add a scenario, then re-run to measure a SKILL.md edit.

Commands

| command | does | |---|---| | skill-harness list --skills <root> | discovered skills + spec status | | skill-harness lint <skill\|all> --skills <root> | validate specs/fixtures; CI gate, exits non-zero on findings | | skill-harness run <skill\|all> --skills <root> [--model p:m ...] [--judge p:m] | run scenarios, grade, score | | skill-harness grade <run-dir> [--judge p:m] | re-grade saved transcripts — no model re-run | | skill-harness review <skill> --skills <root> [--port N] | serve the interactive review UI | | skill-harness add-test <skill> --skills <root> --id ID --title T ... | scaffold a new scenario |

Judge ≠ subject. The judge model must differ from the model under test — same-family grading inflates scores; skill-harness warns when they resemble each other.

lint and list are free (pure static checks, no pi, no API keys); run (and re-grading) spend model tokens.

More

  • Repo + full docs: https://github.com/mojomanyana/skill-harness
  • Step-by-step usage: docs/USAGE.md
  • CI Action (free spec lint on every PR): uses: mojomanyana/skill-harness@v1