Skill: lanyasheng/auto-improvement-learner (version 1.0.0)

--- name: improvement-learner category: learning description: "当需要检查 skill 质量评分、自动优化 SKILL.md 结构、追踪评估分数变化趋势、或「评分低了想知道哪里扣分」时使用。6维结构评估 + HOT/WARM/COLD 三层记忆 + Pareto front。不用于候选语义打分（用 improvement-discriminator）或全流程编排（用 improvement-orchestrator）。" license: MIT triggers: - evaluate skill - self-improve - skill quality check - 评估.*skill - 质量评分 - 哪里扣分 - 自动优化 --- # Improvement Learner Real Karpathy self-improvement loop: evaluate → modify → re-evaluate → keep/revert → repeat. ## When to Use - 查看一个 skill 在 6 个维度上的质量评分 - 运行自动改进循环（Pareto front 保护，不允许任何维度回退） - 追踪 skill 评估分数的历史变化 ## When NOT to Use - **给改进候选打语义分** → use `improvement-discriminator` - **跑全流程（生成→打分→门禁→执行）** → use `improvement-orchestrator` - **只想改一个文件** → use `improvement-executor` ## 6 Evaluation Dimensions | Dimension | Checks | Pure-text default | |-----------|--------|-------------------| | **accuracy** | 15 items: frontmatter(3), symptom-driven desc, When to Use/Not, code examples, Usage, few-shot, no vague language, min length, Related Skills, Output Artifacts, atomicity | — | | **coverage** | SKILL.md = 60% base + scripts/references/tests/README bonuses | — | | **reliability** | pytest pass=1.0, fail=0.5 | 1.0 (pure-text) | | **efficiency** | Line count: ≤200=1.0, ≥1200=0.3 | — | | **security** | No api_key/password/sk- in SKILL.md, no os.system()/exec() | — | | **trigger_quality** | Description length, triggers field, disambiguation | — | ## Three-Layer Memory | Layer | Capacity | Behavior | |-------|----------|----------| | **HOT** | ≤100 | Always loaded, frequently accessed patterns | | **WARM** | Unlimited | Overflow from HOT, loaded on demand | | **COLD** | Archive | >3 months inactive (future) | <example> 正确用法: 评估一个 skill 的质量 $ python3 scripts/self_improve.py --skill-path /path/to/skill --max-iterations 1 → 输出 JSON: {"final_scores": {"accuracy": 0.83, "coverage": 1.0, "reliability": 1.0, ...}} → accuracy 0.83 说明 SKILL.md 缺少部分检查项（如 Output Artifacts 或 Related Skills） </example> <anti-example> 错误判读: 纯文本 skill 的 reliability=1.0 不代表质量好 → 纯文本 skill 没有 scripts/，reliability 默认 1.0（没有代码就不需要测试） → 真正有意义的维度是 accuracy 和 trigger_quality </anti-example> ## CLI ```bash # 评估（不改动，只看分数） python3 scripts/self_improve.py --skill-path /path/to/skill --max-iterations 1 # 自改进循环（5 轮） python3 scripts/self_improve.py \ --skill-path /path/to/skill \ --max-iterations 5 \ --memory-dir /path/to/memory \ --state-root /path/to/state # 追踪历史 python3 scripts/track_progress.py --skill-path /path/to/skill --output progress.json ``` ## Output Artifacts | Request | Deliverable | |---------|------------| | Evaluate | JSON with 6-dimension scores (0.0-1.0 each) | | Self-improve | JSON: iterations, kept/reverted/skipped, final_scores, memory stats | | Track progress | JSON with historical scores and trend data | ## Related Skills - **improvement-discriminator**: Semantic scoring (LLM judge); learner focuses on structural quality - **improvement-orchestrator**: Full pipeline; learner provides standalone quality scoring used by autoloop-controller and self-improvement loop (not a stage in the orchestrator pipeline) - **benchmark-store**: Pareto front data shared between learner and benchmark-store


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)