# self-learning-skills > Memory sidecar for agent work: recall before tasks, record learnings after tasks, review recommendations, optional backport bundles. - Author: scottfalconer - Repository: scottfalconer/self-learning-skills - Version: 20260116121033 - Stars: 37 - Forks: 0 - Last Updated: 2026-02-06 - Source: https://github.com/scottfalconer/self-learning-skills - Web: https://mule.run/skillshub/@@scottfalconer/self-learning-skills~self-learning-skills:20260116121033 --- --- 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`