Individual skill
harness-learn
Run a GEPA learning cycle via `metaharness learn`, optimizing a harness genome against a SWE-bench-style slice. Use when training a harness genome. $0 dry-run by default; `--run` opts into spend. Needs a metaharness checkout (--repo or $METAHARNESS_REPO).
plugins/ruflo-metaharness/skills/harness-learn/SKILL.md
Source commit 722232566ad1fc4e73ba4fefc46d77d380ce61ba
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- Observed behavior
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- Last checked
- Nov 6, 2026
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Repository context
- Repository stars (not skill ratings)
- 81,207
- Repository last updated
- Nov 7, 2026
- License
- MIT
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- swarm Repository topic
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- education Derived category
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Surfaces `metaharness learn` — the upstream GEPA learning harness that
evolves harness policy genomes against a scored task corpus instead of
hand-editing prompts. Candidates are scored on held-out slices and only
measured winners promote (the shipped cand-6 genome is the first such
promotion: holdout gold 2/12 → 3/12, zero regressions).
## When to use
- A harness's policy prompt underperforms on a task family and you want a
measured improvement loop rather than manual prompt iteration.
- Pricing a learning run before committing spend — the default dry-run
resolves the slice manifest and reports cost without any model calls.
- After a learn run promotes a genome: pair with `harness-gepa --op render`
to inspect what the promoted policy actually says.