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

--- name: improvement-generator category: tool description: "当需要为目标 skill 生成改进候选、把上次失败信息注入下一轮生成、或分析历史记忆模式来避免重复失败时使用。支持 --trace 注入失败上下文。不用于打分（用 improvement-discriminator）或评估（用 improvement-learner）。" license: MIT triggers: - generate candidates - propose improvements - 生成候选 - 改进建议 version: 0.1.0 author: OpenClaw Team --- # Improvement Generator Produces ranked improvement candidates from target analysis, feedback signals, and failure traces. ## When to Use - 为目标 skill 生成结构化改进候选 - 把上次失败的 trace 注入下一轮（GEPA trace-aware） - 根据记忆模式避开已经失败过 >=3 次的策略 ## When NOT to Use - **给候选打分** → use `improvement-discriminator` - **评估 skill 结构** → use `improvement-learner` - **全流程** → use `improvement-orchestrator` ## CLI ```bash python3 scripts/propose.py \ --target /path/to/skill \ # REQUIRED: skill directory or single file --state-root /path/to/state \ # default: lib/state_machine.DEFAULT_STATE_ROOT --source memory.json \ # repeatable: feedback/memory/baseline-failures sources --max-candidates 4 \ # default 4: max candidates to generate --trace failure_trace.json \ # inject prior failure trace for retry prioritization --run-id custom-run-id \ # default: auto-generated from target --output candidates.json \ # default: {state-root}/candidate_versions/{run-id}.json --lane generic-skill # default: generic-skill ``` | Param | Default | When to change | |-------|---------|---------------| | `--max-candidates` | 4 | Lower to 2 for fast iteration; raise for diverse exploration | | `--trace` | None | Pass when retrying after gate revert — deprioritizes failed category | | `--source` | [] | Add feedback.jsonl, memory files, or evaluator baseline-failures.json | | `--run-id` | auto | Set explicitly when integrating with external tracking | ## 6 Candidate Categories | Category | Risk | Executor Support | Description | |----------|------|-------------------|-------------| | `docs` | low | Yes (`append_markdown_section`) | Append operator notes/limitations to Markdown docs | | `reference` | low | Yes (`append_markdown_section`) | Add control-plane-friendly notes to reference files | | `guardrail` | low | Yes (`append_markdown_section`) | Add conservative auto-promote rules to guardrail docs | | `prompt` | medium | No | SKILL.md prompt restructure (requires manual review) | | `workflow` | medium | No | Workflow adapter/orchestration hook changes | | `tests` | medium | No | Smoke-check/validation test cases | ## Trace-Aware Generation When `--trace` is provided, `adjust_candidates_from_trace()` deprioritizes the category that failed in the prior run and boosts alternatives: ``` failure_trace.json: {"candidate_id": "cand-01-docs", "reason": "gate rejected"} → docs candidates moved to end, reference/guardrail candidates boosted to front ``` ## Evaluator-Driven Fix (`_find_evaluator_failures` + `_llm_propose_skill_fix`) When `--source` includes a `baseline-failures.json` (type=`evaluator_baseline_failures`), the generator: 1. Reads failed task details (task_id, score, error) 2. Sends current SKILL.md + failures to `claude -p` to get a targeted fix 3. Returns an `eval-fix` candidate as highest priority (risk_level=low, executor_support=True) ## Correction Hotspots (`_find_correction_hotspots`) Scans feedback.jsonl sources for user correction events (`outcome=correction|partial`). Returns `dimension_hint → count` mapping used to prioritize candidates that address the most-corrected dimensions. <example> 正确: 第一次生成 + 有 evaluator baseline failures $ python3 scripts/propose.py --target /path/to/skill --source baseline-failures.json --state-root ./state → 候选 1: LLM-proposed SKILL.md fix targeting failed tasks (category=prompt, risk=low) → 候选 2-4: template candidates (docs, reference, guardrail) → stdout: /state/candidate_versions/run-001.json </example> <anti-example> 错误: 同一个 category 失败 3 次后还继续重试 → 应该用 --trace 注入失败信息让 generator 自动切换到其他 category </anti-example> ## Output Artifact ```json {"schema_version": "1.0", "run_id": "...", "stage": "proposed", "candidates": [{"id": "cand-01-docs", "category": "docs", "risk_level": "low", "execution_plan": {"action": "append_markdown_section", "section_heading": "## Operator Notes", "content_lines": ["..."]}, ...}], "failure_trace_used": false, "truth_anchor": "/state/candidate_versions/run-001.json"} ``` ## Related Skills - **improvement-discriminator**: Scores the candidates this skill produces → called by orchestrator as stage 2 - **improvement-orchestrator**: Calls generator as stage 1, passes `--source` with failure traces - **improvement-evaluator**: Baseline failures fed back as `--source` to inform candidate generation


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