AgentSkillsCN

self-learning-skills

用于代理工作的内存侧车：任务前召回、任务后记录学习、审查推荐、可选回传包。

SKILL.md
--- frontmatter
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 <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>/.agent-skills/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