--- name: self-learning-skills description: "Memory sidecar for agent work: recall before tasks, record learnings after tasks, review recommendations, optional backport bundles." --- # Self-learning sidecar Use this skill to **recall** prior shortcuts before you start work, and to **record** durable “aha” moments + recommendations after you finish. Critical rule: if no learnings exist (cold start), say so and proceed with standard tools — **do not invent memories**. ## CLI path (important) This skill ships an optional helper CLI at `\/scripts\/self_learning.py` (where `` is the directory that contains this `SKILL.md`). - Codex default: `${CODEX_HOME:-$HOME\/.codex}\/skills\/self-learning-skills` - In the commands below, replace `` with your install path. ## 1) PRE-RUN: Recall (before starting work) **When to use:** Before any non-trivial task. **Action:** 1. Locate the project store: `\/.agent-skills\/self-learning\/v1\/users\/\/` 2. Read `\/INDEX.md` (quick skim). 3. If you need targeted recall, run: - `python3 \/scripts\/self_learning.py list --query ""` - Optional filters: `--skill `, `--tag skill:` 4. Summarize **3–7** directly actionable bullets relevant to the current task (titles + IDs only; no long dumps). ## 2) POST-RUN: Record (after finishing work) **When to use:** You discovered something durable (schema, fix, command sequence, constraint, etc.). **Action:** 1. Capture **1–5** Aha Cards (durable, reusable, specific, non-sensitive). Format: `references\/FORMAT.md`. - Ensure every Aha Card and Recommendation has `primary_skill` (use `unknown` if unsure). - Set `scope` to `project` (repo\/run-specific) or `portable` (generally reusable; a backport candidate). - If you rediscovered the same learning, treat it as reinforcement (signal) rather than duplicating the full card. 2. Capture **1–5** concrete recommendations (what to change and where). 3. Persist: - `python3 \/scripts\/self_learning.py record --json payload.json` (or stdin) 4. If you used an existing Aha Card or Recommendation, mark it as used: - `python3 \/scripts\/self_learning.py use --aha aha_...[,aha_...] [--rec rec_...[,rec_...]]` - Or include `used_aha_ids` \/ `used_rec_ids` (or `used: {aha_ids, rec_ids}`) in the `record` payload to auto-append usage signals. **Output requirement:** print a short summary + top 3 items, then point to “view more” (`INDEX.md` \/ `review --format json`). Do not dump long JSON by default. ## 3) REVIEW: Dashboard \/ Next actions **When to use:** “What’s still open?”, “What’s stale?”, “What should we backport?”, “Most useful learnings this week?” **Action:** - `python3 \/scripts\/self_learning.py review --days 7` - Full JSON: add `--format json` - Filters: `--skill `, `--scope project|portable`, `--status proposed,accepted,in_progress`, `--query ""` ## 4) MAINTENANCE \/ Governance - Repair store hygiene (append-only): `python3 \/scripts\/self_learning.py repair --apply` - Update recommendation status\/scope: `python3 \/scripts\/self_learning.py rec-status --id rec_... --status done --scope portable --note "..."` - Optional backport bundle (explicit + auditable): `python3 \/scripts\/self_learning.py export-backport --skill-path --ids [--make-diff] [--apply]` - Inspect backport markers in a skill: `python3 \/scripts\/self_learning.py backport-inspect --skill-path ` ## Docs - Setup\/background: `README.md` - Integration templates (no hooks): `references\/INTEGRATION.md` - Rubric\/format\/portability: `references\/RUBRIC.md`, `references\/FORMAT.md`, `references\/PORTABILITY.md`