harness-evolve

name	harness-evolve
description	Run `@metaharness/darwin evolve <repo>` to mutate a harness's seven policy surfaces (planner/contextBuilder/reviewer/retryPolicy/toolPolicy/memoryPolicy/scorePolicy), sandbox-score each variant, and promote only measured wins. The model is frozen; the harness evolves. Closes the loop ADR-150 opens (score+genome describe; evolve changes). Degrades gracefully when @metaharness/darwin is absent (ADR-150 + ADR-153 architectural constraints).
argument-hint	--repo <path> [--generations 3] [--children 3] [--concurrency 2] [--sandbox real|mock|agent] [--selection pareto|quality-diversity|...] [--mutator deterministic|ruvllm] [--diagnose] [--confirm]
allowed-tools	Bash

Surfaces the upstream metaharness-darwin evolve CLI as a ruflo skill. The write layer that pairs with ADR-150's read layer (score / genome / mcp-scan / threat-model / oia-audit). Use when you have a harness whose readiness scores are flat and you want to discover which surface mutation moves them — without retraining the foundation model.

When to use

• A harness-score result is below target and you don't know which policy surface is responsible.
• You're seeding a harness for a new vertical and want to find a good starting configuration empirically rather than hand-tuning.
• You're comparing your hand-tuned harness against an evolved baseline (treat darwin's champion as the strawman).

When NOT to use

• For continuous background optimization. Darwin Mode is human-initiated. Wire it into CI for one-shot exploration, not for autonomous self-modification.
• For ruflo itself in CI. ADR-153 §5 explicitly rejects auto-evolving ruflo — the CI gate verifies graceful degradation, not convergence.

Algorithm

Implementation: scripts/evolve.mjs.

1. Validate args (--repo exists, caps on --generations ≤ 50, --children ≤ 20, --concurrency ≤ 8, sandbox/selection/mutator are known values).
2. Without --confirm: print plan + exit 0 (mirrors harness-mint safety convention; defense in depth over the upstream safety.ts checks).
3. With --confirm: shell to npx -y @metaharness/darwin@~0.8.0 metaharness-darwin evolve <repo> ... via the shared _darwin.mjs async helper. Per-generation progress is forwarded to stderr; final champion JSON is captured from stdout.
4. Compute timeout from generations × children × per-variant (per-variant ≈ 60s real, ≈ 2s mock). Caller may override with --timeout-ms.
5. Honor upstream exit code 99 — propagate as "safety-disqualified", do not remap. This is a designed-in tripwire (a variant tripped inspectVariant for secrets / shell-out / network / dynamic-eval). See ADR-153 §"Safety model".
6. Optional --alert-on-no-improvement: exit 1 when champion ≤ parent.

The seven mutation surfaces

Surface	What it owns
planner	task decomposition / step ordering
contextBuilder	what gets fed into the prompt
reviewer	self-critique / output verification
retryPolicy	when + how to retry on failure
toolPolicy	which tools the agent may use, under which conditions
memoryPolicy	what to persist, recall, forget
scorePolicy	how the agent grades its own output

One mutation per variant. Multi-surface mutations are not allowed (causal attribution stays clean).

Output

Reports land under <repo>/.metaharness/:

.metaharness/
  archive.json         # full lineage tree (sampling next gen draws from this)
  lineage.json         # parent→child edges only
  variants/<id>/       # per-variant code (kept for audit)
  runs/<id>/           # per-variant sandbox test output
  reports/winner.json  # final champion + score delta vs parent

Skill stdout = JSON {success, data: {champion, plan, durationMs, improved}} (plus data.diagnosis when --diagnose is passed — see below).

Failure diagnosis (--diagnose)

GEPA's key trick is natural-language failure diagnosis from execution traces feeding the next mutation — not just scalar fitness. --diagnose adds a modest slice of that: after the evolution completes, the losing / failed variants' transcripts are run through darwin's GEPA library ops (analyzeTranscript + classifyFailure, via the shared importGepa resolver in scripts/_darwin.mjs) and a diagnosis section is appended to the emitted JSON:

"diagnosis": {
  "available": true,
  "scope": "losing-variants",
  "variants": [
    { "id": "g1_v0", "transcripts": 2,
      "failureClasses": { "exploration-loop": 1, "edit-mechanics": 1 },
      "dominantClass": "exploration-loop" }
  ],
  "totals": { "exploration-loop": 1, "edit-mechanics": 1 }
}

Upstream shape caveats (verified against @metaharness/darwin@0.8.0):

• metaharness-darwin evolve --json prints a TEXT leaderboard — the stdout carries no JSON and no transcripts. Per-variant run records live at <repo>/.metaharness/runs/<id>.json.
• Those run records hold sandbox exec traces ({taskId, exitCode, stdout, stderr}), which are NOT GEPA {actionRaw, obs} transcripts. Diagnosis therefore uses GEPA-shaped transcripts when a run record embeds them (agent sandbox / future upstream), falls back to the champion's transcript, and otherwise emits diagnosis: {available: false, reason, traceSummary} where traceSummary is a mechanical per-variant tally (tasks / failed / timedOut / blockedActions).
• --diagnose NEVER fails the run — any internal error degrades to {available: false, reason: "diagnosis-failed: ..."}.

Exit codes

Code	Meaning
0	Evolved OK, or dry-run, or degraded (Darwin absent)
1	--alert-on-no-improvement and champion did not beat parent
2	Config error or evolution infrastructure failure
99	Upstream "safety-disqualified" (PROPAGATED, not remapped)

Graceful degradation (ADR-150 constraint 3 + ADR-153)

When @metaharness/darwin is not installed, the script emits {degraded: true, reason: 'metaharness-darwin-not-available', hint: ...} and exits 0. ruflo continues to function. CI's no-metaharness-smoke.yml-style job asserts this path.