evolve

Platform Note: State files use the platform's native directory: .claude/ (Claude Code), .codex/ (Codex CLI), or .cursor/ (Cursor IDE). Shared metrics live in .orchestrator/metrics/ (v2) with fallback to <state-dir>/metrics/ for pre-v2.0 legacy data. See skills/_shared/platform-tools.md.

Evolve Skill

Phase 0: Bootstrap Gate

Read skills/_shared/bootstrap-gate.md and execute the gate check. If the gate is CLOSED, invoke skills/bootstrap/SKILL.md and wait for completion before proceeding. If the gate is OPEN, continue to Phase 1.

Do NOT proceed past Phase 0 if GATE_CLOSED. There is no bypass. Refer to `skills/_shared/bootstrap-gate.md` for the full HARD-GATE constraints.

Phase 1: Config & Data Loading

1.1 Read Session Config

Read and parse Session Config per skills/_shared/config-reading.md. Store result as $CONFIG.

1.2 Check Persistence

Extract persistence from $CONFIG. If persistence is false, abort with message:

"Learnings require persistence to be enabled in Session Config. Add persistence: true to your Session Config block (CLAUDE.md for Claude Code, AGENTS.md for Codex CLI)."

1.3 Determine Mode

Read mode from $ARGUMENTS:

• If empty or not provided, default to analyze
• Valid modes: analyze, review, list
• If invalid mode provided, report error and list valid modes

1.4 Load Data

Lazy-create defensive (#185): If .orchestrator/metrics/learnings.jsonl does not exist (pre-#185 repo or bootstrap skipped), create an empty file and emit an info log — do NOT hard-fail:

LEARNINGS_FILE=".orchestrator/metrics/learnings.jsonl"
if [[ ! -f "$LEARNINGS_FILE" ]]; then
  mkdir -p "$(dirname "$LEARNINGS_FILE")"
  : > "$LEARNINGS_FILE"
  echo "info(#185): auto-created $LEARNINGS_FILE (was missing)" >&2
fi

This defensive step is idempotent and cheap — it ensures /evolve analyze|review|list never fails because of a missing artifact file.

1. Read .orchestrator/metrics/sessions.jsonl (session history). If it does not exist, check <state-dir>/metrics/sessions.jsonl as a legacy fallback (where <state-dir> is .claude/, .codex/, or .cursor/ per platform). If neither exists, warn: "No session history found. Run at least one session first."
2. Read .orchestrator/metrics/learnings.jsonl if it exists. If not found, check <state-dir>/metrics/learnings.jsonl as a legacy fallback.
3. Count existing learnings, note any where expires_at < current date (expired)

Phase 2: Mode Dispatch

Route based on mode:

• analyze → Phase 3
• review → Phase 4
• list → Phase 5

---

Phase 3: Analyze Mode (default)

Extract learnings from session history.

Vault Integration: If vault-integration.enabled is true in Session Config, confirmed learnings are mirrored to the configured Obsidian vault after the atomic write (Step 3.5, step 9). See docs/session-config-reference.md for the vault-integration config block.

Step 3.1: Read Session Data

• Read all entries from .orchestrator/metrics/sessions.jsonl (or <state-dir>/metrics/sessions.jsonl if the v2 path does not exist — see Phase 1.4 fallback)
• Parse each JSONL line as JSON
• Sort by completed_at descending (most recent first)
• If no sessions found, abort: "No session data available. Complete at least one session before running evolve."

Step 3.2: Pattern Extraction

For each of the 6 learning types, apply these heuristics:

1. fragile-file (type: fragile-file)

• Look at wave data: if the same file appears in 3+ waves' files_changed within a session, it is fragile
• Cross-session: if a file appears in 3+ different sessions' files_changed, flag it
• Subject = file path (relative to project root)

2. effective-sizing (type: effective-sizing)

• Compare total_agents and total_waves across session types
• Calculate average agents per wave for each session type
• Subject = canonical identifier like deep-session-sizing or feature-session-sizing
• Insight = "Deep sessions average X agents across Y waves" or "Feature sessions work well with X agents/wave"

3. recurring-issue (type: recurring-issue)

• Look at agent_summary — if failed or partial > 0 across multiple sessions, flag
• Check wave quality fields — repeated failures indicate recurring issues
• Subject = issue pattern identifier (e.g., "test-failures-in-wave-execution", "lint-regressions")

4. scope-guidance (type: scope-guidance)

• Cross-reference effectiveness.planned_issues vs effectiveness.completion_rate
• Skip sessions that lack the effectiveness field (early sessions may not have it)
• If completion_rate is consistently 1.0 with N issues, note "N issues per session works well"
• If completion_rate < 0.7, note "scope was too large"
• Subject = optimal-scope-per-session-type

5. deviation-pattern (type: deviation-pattern)

Ownership Reference: See skills/_shared/state-ownership.md. evolve has read-only access to STATE.md.

• Read <state-dir>/STATE.md if it exists and check ## Deviations section
• Cross-reference with session duration vs planned waves
• Subject = pattern name (e.g., "scope-creep-in-feature-sessions", "underestimated-complexity")

6. stagnation-class-frequency (type: stagnation-class-frequency)

• Read stagnation_events from the most recent 5 sessions in sessions.jsonl (skip sessions lacking the field — they predate #84).
• For each (file, error_class) pair appearing in ≥2 sessions, extract a candidate:
  • Subject = <file>:<error_class> (e.g., skills/wave-executor/wave-loop.md:edit-format-friction)
  • Insight = "File has <error_class> stagnation in recent sessions — candidate for pre-edit grounding (#85)."
  • Evidence = " sessions with stagnation_events for this file/class"
• These learnings feed #85 (pre-edit grounding injection) when it ships — high-frequency pairs trigger grounding.

Step 3.2b: Zero Patterns Check

If no patterns were extracted across all 6 types, report: "No patterns found in session history. This can happen with very few sessions or sessions that lack detailed wave/agent data." and skip to end (do not proceed to AskUserQuestion).

Step 3.3: Deduplicate Against Existing Learnings

For each extracted pattern, check if a learning with same type + subject already exists in learnings.jsonl:

• If exists: propose confidence update (+0.15 if confirmed by new evidence, -0.2 if contradicted)
• If new: propose as new learning with confidence 0.5

Step 3.4: Present Findings via AskUserQuestion

Present extracted patterns to the user for confirmation. Use AskUserQuestion with multiSelect: true:

On Codex CLI where AskUserQuestion is unavailable, present as a numbered Markdown list.

AskUserQuestion({
  questions: [{
    question: "Which learnings should be saved?\n\nExtracted patterns from session history:",
    header: "Evolve — Confirm Learnings",
    options: [
      {
        label: "[type] subject",
        description: "insight | evidence: ... | confidence: 0.5 (new) or +0.15 (update)"
      },
      ...
      {
        label: "Skip all",
        description: "Do not save any learnings this time"
      }
    ],
    multiSelect: true
  }]
})

If user selects "Skip all" or selects nothing, abort gracefully: "No learnings saved."

Step 3.5: Write Confirmed Learnings

For confirmed learnings, use atomic rewrite strategy:

1. Read ALL existing lines from .orchestrator/metrics/learnings.jsonl (if exists) into memory. If not found, check <state-dir>/metrics/learnings.jsonl as a legacy fallback. If legacy data is found, it will be migrated to the v2 path on write (step 8).

2. Apply confidence updates for confirmed existing learnings:

   • Increment confidence by +0.15
   • Cap at 1.0
   • Reset expires_at to current date + learning-expiry-days (default: 30)

3. Apply confidence decrements for contradicted learnings (-0.2) — do NOT reset expires_at for contradicted learnings (let them decay naturally)

4. Append new learnings with:

   • id: generate a uuid-v4 (use uuidgen or equivalent)
   • type: one of fragile-file, effective-sizing, recurring-issue, scope-guidance, deviation-pattern, stagnation-class-frequency
   • subject: the pattern subject
   • insight: human-readable description of the pattern
   • evidence: specific data points that support the pattern
   • confidence: 0.5 for new learnings
   • source_session: session ID from which the pattern was extracted
   • created_at: current ISO 8601 date
   • expires_at: current date + learning-expiry-days (default: 30) (ISO 8601)

5. Verify write: Read back the first line of the written file to confirm valid JSON. If read-back fails or is not valid JSON, report error to user.

6. Prune: remove entries where expires_at < current date OR confidence <= 0.0

7. Consolidate duplicates: if same type + subject appears more than once, keep the entry with highest confidence

8. Write entire result back to .orchestrator/metrics/learnings.jsonl with > (atomic rewrite, NOT append >>)

9. Vault mirror (conditional): Check $CONFIG."vault-integration".enabled via jq. If the field is missing or false, skip this step entirely — skill behavior is unchanged.

   If enabled is true:

   a. Check $CONFIG."vault-integration".mode. If mode is off, skip the mirror invocation (treat as disabled). If mode is absent, default to warn.

   b. Resolve the vault directory: use $CONFIG."vault-integration"."vault-dir" if non-null, otherwise fall back to the $VAULT_DIR environment variable. If neither is set, emit a warning and skip.

   c. Invoke the mirror script:

   node "$PLUGIN_ROOT/scripts/vault-mirror.mjs" \
     --vault-dir "<vault-dir>" \
     --source .orchestrator/metrics/learnings.jsonl \
     --kind learning
   

   d. Handle the exit code according to mode:

   • warn (default): on non-zero exit, surface a warning in evolve output (e.g. "Warning: vault mirror failed — learnings saved locally but not mirrored.") but do NOT fail the skill.
   • strict: on non-zero exit, fail the skill immediately and report the error to the user.

   e. On success (exit 0), report: "Mirrored N learnings to <vault-dir>/40-learnings/."

Report: "Saved N new learnings, updated M existing. Total active: K."

---

Phase 4: Review Mode

Interactive management of existing learnings.

Step 4.1: Load Learnings

• Read .orchestrator/metrics/learnings.jsonl. If not found, check <state-dir>/metrics/learnings.jsonl as a legacy fallback.
• If neither exists or both are empty: "No learnings found. Run /evolve analyze first."
• Parse each line as JSON

Step 4.2: Display Learnings

Present a formatted table grouped by type:

## Active Learnings

| # | Type | Subject | Confidence | Expires | Insight |
|---|------|---------|------------|---------|---------|
| 1 | fragile-file | src/lib/auth.ts | 0.80 | 2026-07-05 | Changed in 4 of last 5 sessions |
| 2 | effective-sizing | feature-session-sizing | 0.65 | 2026-06-20 | Feature sessions work well with 3 agents/wave |
| ... | ... | ... | ... | ... | ... |

Summary: N active learnings (M high confidence, K expiring soon)

Step 4.3: Interactive Management

Use AskUserQuestion with options:

On Codex CLI where AskUserQuestion is unavailable, present as a numbered Markdown list.

AskUserQuestion({
  questions: [{
    question: "What would you like to do with your learnings?",
    header: "Evolve — Review",
    options: [
      { label: "Boost confidence", description: "Select learnings to boost (+0.15)" },
      { label: "Reduce confidence", description: "Select learnings to reduce (-0.2)" },
      { label: "Delete specific learnings", description: "Select learnings to remove" },
      { label: "Extend expiry", description: "Reset expires_at by learning-expiry-days from now" },
      { label: "Done — no changes", description: "Exit without changes" }
    ]
  }]
})

If user selects "Boost confidence", "Reduce confidence", "Delete specific learnings", or "Extend expiry", present a follow-up AskUserQuestion with multiSelect: true listing all learnings by # | type | subject so the user can select which ones to modify.

On Codex CLI where AskUserQuestion is unavailable, present as a numbered Markdown list.

Step 4.4: Apply Changes

Use the same atomic rewrite strategy as Phase 3, Step 3.5:

1. Read all lines from learnings.jsonl
2. Apply the selected operation to selected learnings:
   • Boost: +0.15 confidence (cap 1.0), reset expires_at to +learning-expiry-days
   • Reduce: -0.2 confidence
   • Delete: remove selected entries
   • Extend: reset expires_at to current date + learning-expiry-days
3. Prune entries where expires_at < current date OR confidence <= 0.0
4. Consolidate duplicates (same type + subject): keep highest confidence
5. Write entire result back with > (atomic rewrite)

Report: "Updated N learnings. Total active: K."

---

Phase 5: List Mode

Simple read-only display.

Step 5.1: Load and Display

• Read .orchestrator/metrics/learnings.jsonl. If not found, check <state-dir>/metrics/learnings.jsonl as a legacy fallback.
• If neither exists: "No learnings yet. Run /evolve analyze to extract patterns from session history."
• Parse each line as JSON

Step 5.2: Formatted Output

Display a formatted table grouped by type:

## Active Learnings

### fragile-file
| Subject | Confidence | Expires | Insight |
|---------|------------|---------|---------|
| ... | ... | ... | ... |

### effective-sizing
| Subject | Confidence | Expires | Insight |
|---------|------------|---------|---------|
| ... | ... | ... | ... |

(repeat for each type that has entries)

Step 5.3: Summary

Display summary line:

N active learnings (M high confidence, K expiring soon)

• High confidence = confidence > 0.7
• Expiring soon = expires_at within 14 days of current date

---

Critical Rules

• NEVER modify learnings.jsonl without reading it first — race condition prevention
• NEVER skip the deduplication check — duplicates degrade the intelligence system
• NEVER write learnings without user confirmation — always present via AskUserQuestion first (on Codex CLI where AskUserQuestion is unavailable, present as a numbered Markdown list)
• ALWAYS use uuid-v4 for new learning IDs (generate via uuidgen or equivalent bash command)
• ALWAYS set expires_at to current date + learning-expiry-days from config (default: 30) for new learnings
• ALWAYS present findings to user before writing — no silent writes
• ALWAYS use atomic rewrite (read all, modify, write all with >) — never append with >>
• ALWAYS cap confidence at 1.0 — never exceed

Anti-Patterns

• DO NOT write learnings without user confirmation — always present via AskUserQuestion first (on Codex CLI where AskUserQuestion is unavailable, present as a numbered Markdown list)
• DO NOT append to learnings.jsonl — always use atomic rewrite (read all, modify, write all)
• DO NOT create duplicate learnings — always check type + subject match first
• DO NOT set confidence above 1.0 or forget to cap it
• DO NOT fabricate patterns — only extract from actual session data with verifiable evidence
• DO NOT skip the pruning step — expired and zero-confidence entries must be removed on every write