self-learning-skills

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 <SKILL_DIR>/scripts/self_learning.py (where <SKILL_DIR> is the directory that contains this SKILL.md).

• Codex default: ${CODEX_HOME:-$HOME/.codex}/skills/self-learning-skills
• In the commands below, replace <SKILL_DIR> 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: <repo-root>/.agents/memory/self-learning/v1/users/<user>/
2. Read <project_store>/INDEX.md (quick skim).
3. If you need targeted recall, run:
   • python3 <SKILL_DIR>/scripts/self_learning.py list --query "<keywords>"
   • Optional filters: --skill <name>, --tag skill:<name>
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 <SKILL_DIR>/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 <SKILL_DIR>/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 <SKILL_DIR>/scripts/self_learning.py review --days 7
• Full JSON: add --format json
• Filters: --skill <name>, --scope project|portable, --status proposed,accepted,in_progress, --query "<keywords>"

4) MAINTENANCE / Governance

• Repair store hygiene (append-only): python3 <SKILL_DIR>/scripts/self_learning.py repair --apply
• Update recommendation status/scope: python3 <SKILL_DIR>/scripts/self_learning.py rec-status --id rec_... --status done --scope portable --note "..."
• Optional backport bundle (explicit + auditable): python3 <SKILL_DIR>/scripts/self_learning.py export-backport --skill-path <skill-dir> --ids <aha_ids> [--make-diff] [--apply]
• Inspect backport markers in a skill: python3 <SKILL_DIR>/scripts/self_learning.py backport-inspect --skill-path <skill-dir>

Docs

• Setup/background: README.md
• Integration templates (no hooks): references/INTEGRATION.md
• Rubric/format/portability: references/RUBRIC.md, references/FORMAT.md, references/PORTABILITY.md