skill-self-evolution-enhancer

# Skill Self-Evolution Enhancer This skill enables **other skills** to gain self-evolution capabilities similar to self-improving-agent. A skill that originally has no self-evolution will, after enhancement, have: logging, learning from user feedback, promotion to rules, and a Review→Apply→Report loop—all tailored to its domain. ## Quick Reference | Step | Action | |------|--------| | User requests evolution for skill X | Read target skill's SKILL.md | | Deep analysis | Identify capabilities, scenarios, evolution directions | | Extract domain | Name, use cases, triggers, areas, promotion targets | | Generate .learnings/ | Domain-specific LEARNINGS.md, ERRORS.md, FEATURE_REQUESTS.md | | Generate EVOLUTION.md | Triggers, Review-Apply-Report, OpenClaw feedback rules | | Language | Match target skill's user language (infer from SKILL.md) | ## When to Use - User says: "给 skill X 加上自进化能力" / "Add self-evolution to skill X" - Scaling self-improvement across many skills (each with its own evolution direction) - Target skill is non-coding (e.g., 洗稿能手, 电脑加速) and needs domain-specific triggers ## Workflow ### Step 1: Read Target Skill ``` Read(target_skill_path/SKILL.md) ``` Obtain path from user or infer (e.g., `skills/xxx`, `~/.cursor/skills/xxx`). ### Step 2: Deep Capability & Scenario Analysis **Before generating** any config, analyze the target skill deeply: **Capabilities** (what the skill does): - Primary outputs and workflows - Secondary or edge capabilities - Dependencies (tools, APIs, formats) **Scenarios** (when and how it is used): - User personas - Typical tasks (e.g., 科普改写 vs 汇报改写) - Input/output patterns **Evolution directions** (what can improve): - User feedback patterns (e.g., "改得不通顺" → style) - Failure modes (e.g., "优化无效" → strategy) - Recurring corrections → domain-specific rules **Use cases** → infer from description, Quick Reference, examples ### Step 3: Extract Domain Config When reading the target skill, extract: | Field | Where to Find | Example | |-------|---------------|---------| | **Domain name** | `name` in frontmatter, title | 洗稿能手, 电脑加速 | | **Use cases / scenarios** | Description, Quick Reference, examples | 科普、汇报、直播 | | **Learning triggers** | User feedback phrases in examples | "改得不通顺", "不像口播", "风格不对" | | **Error triggers** | Failure modes | "优化无效", "某些电脑不适用", "报错" | | **Areas** | Output types, workflow stages | 文案/口播/短视频脚本, 或 系统优化/卡顿/报错 | | **Promotion targets** | Skill-specific rules | `{skill}-专属进化规则.md`, `{skill}-最佳实践.md` | **Language**: Infer from SKILL.md content (Chinese vs English). Generate all output files in that language. Use [assets/DOMAIN-CONFIG-TEMPLATE.md](assets/DOMAIN-CONFIG-TEMPLATE.md) to structure the extracted data. ### Step 4: Generate .learnings/ Create inside target skill directory: `target_skill_path/.learnings/` **Structure** (same as self-improving-agent): - `.learnings/LEARNINGS.md` - `.learnings/ERRORS.md` - `.learnings/FEATURE_REQUESTS.md` Use templates from `assets/`; parameterize with domain areas, categories, promotion targets. Write in the target skill's language. ### Step 5: Generate EVOLUTION.md Create `target_skill_path/EVOLUTION.md` using [assets/EVOLUTION-RULES-TEMPLATE.md](assets/EVOLUTION-RULES-TEMPLATE.md). **Must include**: - Quick Reference: domain triggers → actions - **Review→Apply→Report** loop (see below) - Detection triggers (when to log) - Promotion decision tree - Area tags - Domain-specific activation conditions (for hooks) - Experience invalidation / update rules (when user corrects again) ### Step 6: Optional – Activator Script If target skill has `scripts/`, add `scripts/activator.sh` with domain-specific reminder text. Adapt from self-improving-agent; replace generic prompts with domain triggers. ## Review → Apply → Report Loop The enhanced skill must **use** learnings, not only log them. Include this in EVOLUTION.md or the enhanced skill's instructions: ### Before Task - Load relevant entries from `.learnings/LEARNINGS.md` (and ERRORS.md if applicable) - Filter by area, tags, or keywords - Note which entries apply to the current task ### During Task - Apply learnings when relevant - Optionally annotate output: "本次参考了 [LRN-xxx]: ..." (or equivalent in target language) ### After Task - Summarize for user: which learnings were used, what evolution result, what improvement - Let OpenClaw decide: per-use mention vs end-of-task summary **Example** (Chinese): "本次改写了口播稿，参考了经验 [LRN-20250115-001]（科普场景应避免过于书面），相比之前更口语化。" **Example** (English): "Used learning [LRN-20250115-001] (avoid formal tone for科普) in this rewrite; output is more conversational than before." ## User Preference vs Domain Best Practice | Type | Storage | Example | |------|---------|---------| | **User preference** | MEMORY.md (user-level) | "This user prefers shorter sentences" | | **Domain best practice** | `.learnings/LEARNINGS.md` | "科普场景应避免过于书面" | Evolution is driven by **user feedback**; log and promote based on user corrections and recurring patterns. ## OpenClaw Active Feedback Add to the enhanced skill or SOUL.md/AGENTS.md: - When using experience from `.learnings/`, briefly tell the user - At end of task, optionally summarize: evolution used, improvements - Let OpenClaw decide when to surface (per-use vs summary) See [references/openclaw-feedback.md](references/openclaw-feedback.md) for SOUL.md and AGENTS.md snippets. ## Experience Invalidation & Update When user corrects again after a learning was applied: - Add `Contradicted-By: LRN-YYYYMMDD-XXX` to the original entry - Mark `Last-Valid` or `Status: superseded` if the learning is no longer valid - Increment `Recurrence-Count` if the pattern recurs but the fix is different Include in LEARNINGS template: `Recurrence-Count`, `Last-Valid`, `Contradicted-By`. ## Domain Extraction Framework ### Trigger Extraction **Learning triggers** (user feedback → log to LEARNINGS.md): - Look for: "用户说", "when user says", example dialogs - Infer: common corrections, style mismatches, scene-specific preferences - Add generic fallbacks: "不对", "不是这样", "改一下" **Error triggers** (failures → log to ERRORS.md): - Look for: "失败", "报错", "不适用", "when X fails" - Infer: environment-specific failures, edge cases - Add generic fallbacks: "操作失败", "未达到预期" ### Area Mapping Define 3–6 areas that partition the skill's scope. Use domain-specific areas, not coding areas. ### Promotion Target Naming - `{skill-name}-专属进化规则.md` — evolution rules, style preferences - `{skill-name}-最佳实践.md` — best practices - `{skill-name}-安全规范.md` — safety constraints (e.g., 电脑加速) Use kebab-case for skill name in filenames. ## Logging Format (Reuse from Self-Improving-Agent) ID format: `LRN-YYYYMMDD-XXX`, `ERR-YYYYMMDD-XXX`, `FEAT-YYYYMMDD-XXX` Statuses: `pending` | `in_progress` | `resolved` | `wont_fix` | `promoted` | `promoted_to_skill` For full entry formats, see the self-improving-agent skill's Logging Format section. ## References - [assets/DOMAIN-CONFIG-TEMPLATE.md](assets/DOMAIN-CONFIG-TEMPLATE.md) — Schema for domain config - [assets/EVOLUTION-RULES-TEMPLATE.md](assets/EVOLUTION-RULES-TEMPLATE.md) — EVOLUTION.md template - [references/domain-examples.md](references/domain-examples.md) — 洗稿能手, 电脑加速 examples - [references/openclaw-feedback.md](references/openclaw-feedback.md) — SOUL.md, AGENTS.md snippets for active feedback - [scripts/generate-evolution.sh](scripts/generate-evolution.sh) — Optional scaffold generator ## Source - Based on: self-improving-agent 3.0.1 - Purpose: Enable any skill to gain self-evolution capabilities similar to self-improving-agent