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.

Preconditions (upstream design)

The learning harness (GEPA + SWE-bench + Docker) is too heavy for the npm package, so learn needs a local clone:

harness-learn

When to use

Preconditions (upstream design)