/evolve

name: evolve
license: MIT
description: >-
  Research-driven multi-cycle improvement director. Forms causal hypotheses
  about why scores are low, validates them with scout agents before attacking,
  dispatches axis-parallel fleet attacks, extracts transferable patterns, and
  runs indefinitely within a budget envelope. Accumulates a persistent belief
  model and pattern library across sessions.
user-invocable: true
auto-trigger: false
trigger_keywords:
  - evolve
  - sustained improve
  - improvement director
  - research-driven improve
  - multi-cycle improve
  - run until done
  - improve until ceiling
  - keep improving
  - hypothesis
  - belief model
  - scout agents
last-updated: 2026-05-03

/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.

| Artifact | Path | Contents | |---|---|---| | Director state | `.planning/evolve/{target}/director-state.json` | cycle count, spend, velocity history, current phase, halt status | | Belief model | `.planning/evolve/{target}/belief-model.jsonl` | one record per (axis, skill) per cycle: score, hypothesis, evidence, confidence | | Experiment log | `.planning/evolve/{target}/experiment-log.jsonl` | every experiment: hypothesis → prediction → actual delta → mechanism confirmed | | Pattern library | `.planning/evolve/{target}/pattern-library.md` | transferable patterns: what change to what axis class caused what delta in which skills | | Cycle digest | `.planning/evolve/{target}/cycle-{n}-digest.md` | human-readable per-cycle summary for review | | Global patterns | `.planning/research/patterns.md` | cross-target patterns written outside campaign scope; available to future sessions and other targets | | Knowledge wiki | `.planning/wiki/` | compiled wiki pages from `/learn`; integrates evolve discoveries across sessions |

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 =