Evolve

/evolve — Improvement Director

Orientation

Use when: You want sustained autonomous quality advancement — the director forms hypotheses, scouts before attacking, and builds a belief model that compounds across cycles. Runs until a natural ceiling, budget exhaustion, or you say stop.

Don't use when: You want a single scored loop (/improve), a known axis attacked directly (/improve --axis), or a one-time audit (/improve --score-only).

Key difference from /improve: /improve follows the rubric mechanically. /evolve asks why scores are where they are, validates those theories before spending fleet budget, and extracts cross-skill patterns that propagate to skills never directly attacked.

Invocation

/evolve {target}                  # run until ceiling, velocity drop, or budget
/evolve {target} --n={N}          # exactly N director cycles then stop
/evolve {target} --budget=${X}    # run until cumulative spend reaches $X
/evolve {target} --continue       # resume from saved director state
/evolve {target} --status         # show belief model, velocity, spend — no attack
/evolve {target} --axis={name}    # focus director on one axis (scout + attack only)

target maps to .planning/rubrics/{target}.md. If no rubric exists, run /improve {target} Phase 0 first — /evolve requires an approved rubric and will not auto-generate one.

Campaign Artifacts

All findings are externalized incrementally — written after every phase, not only at cycle end. A crashed or compacted session resumes with full context.

Create .planning/evolve/{target}/ on first invocation. Create .planning/research/ if absent.

Cycle digest contents: scores table (axis, prior, this cycle, delta), hypotheses table (id, axis, hypothesis, scout result, confidence), what was attacked (axis, skill, delta, mechanism confirmed), patterns discovered this cycle, belief model updates, and the spend/velocity line. Full template: docs/QUALITY_LOOPS.md#cycle-digest-format.

Director Cycle Protocol

Phase 1: Survey

Run /improve {target} --score-only. Record scores to belief model with delta from prior cycle (empty on cycle 1). Flag any axis that dropped since last cycle as regression-watch — these are checked first in Phase 2.

Phase 2: Hypothesize

For every axis below 8.0, generate one primary hypothesis in this form:

HYPOTHESIS: {axis} scores {n}/10 because {specific mechanism},
            not because {common misread}.
PREDICTION: Fixing {mechanism} will raise score ≥ {delta} across {N} skills.
FALSIFICATION: If we apply {change} and score does not rise > 0.5, hypothesis rejected.

Draw hypotheses from: evaluator justifications in Phase 1, prior evidence in the belief model, and programmatic check failures. Do not hypothesize from score alone — the number is the symptom.

Write each hypothesis to the experiment log as { id, status: "pending", ... }.

Skip hypothesis generation for an axis if the belief model already has a confidence >= 0.8 confirmed hypothesis for it that has not yet been attacked.

Phase 3: Scout

For axes below 7.0, or axes with unconfirmed hypotheses: dispatch one scout agent per hypothesis. Scouts read — they do not modify files.

Each scout returns { "hypothesis_id", "confirmed", "evidence", "confidence" } (schema example: docs/QUALITY_LOOPS.md#scout-result-schema).

Scout confidence protocol: Scouts read relevant files only — no edits, no test runs. Assign confidence:

• 0.9+: mechanism is directly observable (explicit absence, missing section, wrong value in file)
• 0.7–0.89: strong indirect evidence from 2+ corroborating observations
• 0.4–0.69: single observation that supports the hypothesis; alternative explanations plausible
• < 0.4: no direct evidence found; hypothesis is speculative from this file set

Run scouts in parallel. Update experiment log:

• confidence >= 0.7 → confirmed
• confidence 0.4–0.69 → needs-evidence (do not attack; add to next cycle)
• confidence < 0.4 → rejected

Skip Phase 3 for any hypothesis already confirmed at confidence >= 0.8 in the belief model from a prior cycle.

Phase 4: Prioritize

For each confirmed hypothesis compute:

EV = (delta_estimate × axis_weight × confidence) / (effort_tier × collision_multiplier)

• effort_tier: low=1.0, medium=1.5, high=2.5
• collision_multiplier: 2.0 if axis shares primary files with another attack in this cycle

Select top K axes where K = min(confirmed count, 4). Document selection rationale in cycle digest. If --axis was set, skip ranking — attack only that axis.

Phase 5: Fleet Attack

Dispatch one agent per selected axis in an isolated worktree (Agent tool, isolation: "worktree"). Each agent receives:

• The confirmed hypothesis and its falsification criterion
• The specific files to modify
• Verification oracle: node scripts/run-with-timeout.js 300 node scripts/test-all.js

Each agent returns a structured result: { "axis", "skill", "delta", "mechanism_confirmed", "files_changed", "approach" } (schema example: docs/QUALITY_LOOPS.md#fleet-agent-result-schema).

Merge rules:

• Non-conflicting worktrees: merge all
• Conflicting worktrees (same file): keep higher delta, discard lower
• Regression on any previously passing programmatic check: abort that worktree, do not merge
• mechanism_confirmed: false (score improved but not via predicted mechanism): record as incidental_improvement, mark hypothesis as needs-revision

Commit each merged worktree with a message citing the hypothesis ID.

After committing changes to any SKILL.md (here or in Phase 7), run /reload-skills if the running Claude Code version supports it so the change is live this session; otherwise note that a fresh session is required before the updated skill takes effect.

Phase 6: Synthesize

For each result:

1. Update belief model — append evidence record for (axis, skill)
2. Update experiment log — mark verified / refuted / incidental
3. Identify transferable patterns:

PATTERN: {axis_class} | Mechanism: {what caused improvement} | Delta: {avg} across {N} instances | Applies to: {skill list} | Confidence: high/medium/low

Write patterns to .planning/evolve/{target}/pattern-library.md.

Compile into wiki: After writing to the pattern library, call /learn --from-evolve {target} --cycle {n}. This compiles cycle discoveries into .planning/wiki/ — integrating with findings from prior cycles and campaigns rather than siloing them in the evolve directory. Skip if /learn is not available in this session (log the skip, do not block the cycle).

Phase 7: Cross-Pollinate

For each confidence: high pattern, or any pattern confirmed in 2+ skills: apply to all other applicable skills as targeted single-file edits — without running a full attack cycle.

Run verification oracle per cross-pollinated skill. Commit only if all programmatic checks pass and no axis drops > 0.3. Revert on regression; mark pattern as context-dependent.

Write patterns that apply beyond this target to .planning/research/patterns.md.