---
name: evolve
user-invocable: false
tags: [learning, intelligence, meta]
model: sonnet
model-preference: sonnet
model-preference-codex: gpt-5.4-mini
model-preference-cursor: claude-sonnet-4-6
description: >
  Use this skill when extracting session patterns into reusable learnings. Three modes: analyze (extract from session history),
  review (edit/manage existing learnings), list (display active learnings). Manages .orchestrator/metrics/learnings.jsonl.
---

> **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.

<HARD-GATE>
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.
</HARD-GATE>

## 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:

```bash
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 8 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 <X> has <error_class> stagnation in <N> recent sessions — candidate for pre-edit grounding (#85)."
  - Evidence = "<N> sessions with stagnation_events for this file/class"
- These learnings feed #85 (pre-edit grounding injection) when it ships — high-frequency pairs trigger grounding.

#### 7. hardware-pattern (type: `hardware-pattern`)

> **v3.1.0 / Sub-Epic #160 (C2, issue #171).** Keyed on `host_class` rather than project — surfaces hardware-bound problems that affect the user across every repo on the same machine. Complements the project-keyed types above.

- Read `.orchestrator/metrics/events.jsonl` (session + wave events) and the registry `sweep.log` at `~/.config/session-orchestrator/sessions/sweep.log`. Both are optional — missing files produce no candidates.
- Invoke `scripts/lib/hardware-pattern-detector.mjs` → `detectHardwarePatterns({events, sweepLogEntries, thresholds})`. Thresholds come from Session Config `resource-thresholds` when present, falling back to `DEFAULT_THRESHOLDS`.
- Five detection signals (aggregated per `(signal, host_class)` pair, ≥2 occurrences required):
  - **oom-kill** — `orchestrator.session.stopped` with `exit_code: 137` or OOM-marker in `error`
  - **heartbeat-gap** — registry sweep-log entries with `gap_minutes` above `resource-thresholds.zombie-threshold-min`
  - **concurrent-session-pressure** — session-start events with `peer_count ≥ concurrent-sessions-warn`
  - **disk-full** — events whose `error` matches `ENOSPC` / "no space left"
  - **thermal-throttle** — events whose `resource_snapshot.cpu_load_pct` crosses `cpu-load-max-pct`
- Each candidate is piped through `candidateToLearning()` → `validateLearning()`. Default `scope` is `private` (in-repo only). To promote to `public`, the user runs `npm run share:hw-learnings -- --promote` (C3 export). This anonymizes each `private` hardware-pattern entry, validates via the privacy contract, and appends a `public` twin to `learnings.jsonl` (original preserved). Use `--dry-run` to preview without writing.
- Subject convention: `<signal>::<host_class>` (e.g., `oom-kill::macos-arm64-m3pro`). The `::` separator avoids colliding with project-keyed subjects.
- Confidence starts at 0.5 like other learning types, but decay is slower in practice: hardware stays the same longer than code. This is an emergent property of the existing expire-after-N-days policy applied to a mostly-stable `host_class` — no special-casing needed.
- **Presentation in step 3.5** (see below): render hardware-patterns in a dedicated section titled `## Hardware Patterns (keyed on host_class)` after the project-keyed patterns. This makes the source of the learning obvious to the user at confirmation time.

#### 8. autopilot-effectiveness (type: `autopilot-effectiveness`)

> **v3.2 Autopilot / Sub-Epic #271 (issue #298).** Compares manual vs. autopilot session outcomes per mode (housekeeping, feature, deep) so the loop can learn whether walk-away runs preserve quality. Complements the project-keyed and hardware-keyed types above.

- Read `.orchestrator/metrics/autopilot.jsonl` (one record per autopilot loop run) **and** `.orchestrator/metrics/sessions.jsonl` (manual + autopilot session outcomes). Both are optional — missing files produce no candidates.
- Invoke `scripts/lib/evolve/autopilot-effectiveness.mjs` → `analyze(autopilotRuns, sessions)`. The module pairs records by `mode` and compares completion-rate, carryover-rate, kill-switch frequency, and quality-gate pass-rate between the two populations.
- **Data-gating contract:** the analyzer requires **≥20 paired manual+autopilot runs per mode** before emitting any candidates. Below that threshold the function returns `[]` (empty input contract) — evolve simply skips this type for that mode and reports nothing. This prevents premature conclusions from small samples (#297 calibration depends on the same threshold).
- Subject convention: `<mode>-manual-vs-autopilot` (e.g., `housekeeping-manual-vs-autopilot`, `feature-manual-vs-autopilot`, `deep-manual-vs-autopilot`). One subject per mode that crosses threshold.
- Insight = "Autopilot <mode> sessions complete at <X>% vs. manual <Y>% (Δ <Z>pp across N pairs)" or analogous carryover/kill-switch framing when those signals dominate.
- Confidence starts at 0.5 like other learning types; lifecycle ±0.15 / -0.20 via the existing dedupe-and-update infrastructure in Step 3.3 — no special-casing.
- Each candidate is piped through `candidateToLearning()` → `validateLearning()` exactly like the other types. Default `scope` is `private` (autopilot RUN data is per-host until the user opts in to share). (refs #298)

### Step 3.2b: Zero Patterns Check

If no patterns were extracted across all 8 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 the **canonical schema_version:1 shape** — every field is required (#303):
   - `schema_version`: **1** (integer, ALWAYS — never omit)
   - `id`: UUID v4 string generated via `node -e "const {randomUUID}=require('crypto');process.stdout.write(randomUUID())"` or `uuidgen | tr '[:upper:]' '[:lower:]'`. MUST be a non-empty UUID string. **Never omit** — missing `id` causes 100% mirror-skip (#303).
   - `type`: one of `fragile-file`, `effective-sizing`, `recurring-issue`, `scope-guidance`, `deviation-pattern`, `stagnation-class-frequency`, `hardware-pattern`, `autopilot-effectiveness`
   - `subject`: the pattern subject
   - `insight`: human-readable description of the pattern. **MUST be `insight`** — do NOT use `description` or `recommendation` (legacy alias keys that vault-mirror cannot read; see #303).
   - `evidence`: specific data points that support the pattern
   - `confidence`: 0.5 for new learnings
   - `source_session`: **non-empty kebab-slug string** identifying the session from which the pattern was extracted (e.g. `main-2026-04-27-1942`). MUST be a string — never an object, array, number, or null. If multiple sessions contributed, use the earliest. If unknown, use `"unknown"` (the string). **Never** pass `String(<object>)` — that yields `"[object Object]"` and breaks the YAML mirror downstream (#307). Optional pre-write validation: `jq -e 'select(.source_session | type == "string" and length > 2)'`.
   - `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. Derive a synthetic `EVOLVE_SESSION_ID` so the vault-mirror auto-commit phase (#31) produces a traceable commit subject (`chore(vault): mirror evolve-<date> — N learnings + 0 sessions`):
      ```bash
      EVOLVE_SESSION_ID="evolve-$(date -u +%Y-%m-%d-%H%M)"
      node "$PLUGIN_ROOT/scripts/vault-mirror.mjs" \
        --vault-dir "<vault-dir>" \
        --source .orchestrator/metrics/learnings.jsonl \
        --kind learning \
        --session-id "$EVOLVE_SESSION_ID"
      ```

   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
