From ruflo-metaharness
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).
How this skill is triggered — by the user, by Claude, or both
Slash command
/ruflo-metaharness:harness-evolve --repo <path> [--generations 3] [--children 3] [--concurrency 2] [--sandbox real|mock|agent] [--selection pareto|quality-diversity|...] [--mutator deterministic|ruvllm] [--diagnose] [--confirm]--repo <path> [--generations 3] [--children 3] [--concurrency 2] [--sandbox real|mock|agent] [--selection pareto|quality-diversity|...] [--mutator deterministic|ruvllm] [--diagnose] [--confirm]This skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Surfaces the upstream `metaharness-darwin evolve` CLI as a ruflo skill. The
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.
harness-score result is below target and you don't know which policy
surface is responsible.Implementation: scripts/evolve.mjs.
--repo exists, caps on --generations ≤ 50, --children
≤ 20, --concurrency ≤ 8, sandbox/selection/mutator are known values).--confirm: print plan + exit 0 (mirrors harness-mint safety
convention; defense in depth over the upstream safety.ts checks).--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.generations × children × per-variant (per-variant
≈ 60s real, ≈ 2s mock). Caller may override with --timeout-ms.inspectVariant
for secrets / shell-out / network / dynamic-eval). See ADR-153 §"Safety model".--alert-on-no-improvement: exit 1 when champion ≤ parent.| 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).
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).
--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/[email protected]):
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.{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: ..."}.| 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) |
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.
Guides completion of development work by verifying tests, detecting environment, and presenting structured options for merge, PR, or cleanup.
Guides creation and editing of skills using test-driven development with pressure scenarios and subagents to verify agent compliance.
Dispatches multiple subagents concurrently for independent tasks without shared state. Use when facing 2+ unrelated failures or subsystems that can be investigated in parallel.
4plugins reuse this skill
First indexed Jul 14, 2026
npx claudepluginhub meefs/claude-code-flow --plugin ruflo-metaharness