Skill: bustes01/self-iteration-engine (version 1.0.0)

--- name: self-iteration-engine description: > Self-iteration and feedback learning engine for AI agent skills. Tracks usage logs, detects performance patterns, triggers skill updates, and proposes new skill creation based on repeated request patterns. Use when: any skill needs to improve over time, detect failure patterns, learn from user corrections, or decide when to create new skills. Triggers: "learn from this", "improve yourself", periodic review cycles, or any skill declaring dependency for self-improvement. version: 1.0.0 metadata: openclaw: emoji: "🔄" homepage: https://clawhub.ai/BusTes01/self-iteration-engine models: - gpt-4 - deepseek-v4-flash - gemini-2.0-flash - claude-4-opus --- # 🔄 Self-Iteration Engine Self-iteration and feedback learning engine for AI agent skills. Tracks usage logs, detects performance patterns, triggers skill updates, and proposes new skill creation based on repeated request patterns. This is a **shared component skill** — other skills reference it for self-improvement. When updating, ensure backward compatibility with all dependent skills. Users may install this skill standalone for its capabilities. ## Usage Log Format Maintain a usage log file for each skill that declares dependency: ```markdown # Usage Log: <skill-name> ## [YYYY-MM-DD] - Request: <brief description> - Outcome: success | partial | fail - User corrected: yes | no - Correction detail: <if yes, what was corrected> - Lesson: <what to do differently next time> ``` File location: `memory/usage-logs/<skill-name>.md` ## Self-Iteration Triggers Evaluate these conditions during each **periodic review** (default daily, configurable): | Condition | Action | |-----------|--------| | 3+ consecutive successful invocations | Mark skill as "stable" — reduce context allocation | | 2+ failures for the same scenario | Flag for SKILL.md reassessment | | Same request type appears 3+ times | Evaluate creating a new dedicated skill | | User corrected output | Log correction, adjust future behavior for that scenario | | Skill hasn't been reviewed in 30+ days | Trigger review: check if dependencies changed, update examples | | External tech change detected | Compare against skill's core technology stack, update if needed | ## Feedback Loop Implementation ```yaml # memory/feedback-loop/<skill-name>.yaml feedback_loop: last_review: "2026-05-19" next_review: "2026-05-26" status: "stable" | "needs-attention" | "monitoring" patterns_observed: - pattern: "user asks for financial data on weekends" current_response: "check if markets are open" improvement: "pre-fill with last trading day data" status: "resolved" | "pending" | "in-progress" skill_performance: total_calls: 47 success_rate: 0.96 issues: - "data freshness on weekends" ``` ## Review Cycle ### Daily (lightweight) - Scan today's usage log entries - Check for failure patterns - Log any user corrections ### Weekly (moderate) - Aggregate performance stats - Check iteration triggers (listed above) - If any trigger fires → update SKILL.md or create new skill - Archive usage logs older than 7 days ### Monthly (deep) - Full performance review across all skills - Compare success rates, identify declining trends - Check if any external technology replaced the skill's core stack - Propose new skill ideas based on accumulated pattern data - Run memory cleanup (delegate to complex-memory-manager) ## Update Decision Matrix | Signal | Decision | |--------|----------| | 80%+ success rate, no user corrections | No update needed | | 60-80% success rate | Minor update: clarify instructions, add edge cases | | <60% success rate | Major update: redesign workflow, check data sources | | User corrects same thing 3+ times | Fix that specific guidance in SKILL.md | | External API / tool changed | Update immediately | | New competing technology available | Evaluate migration; update if 2x+ better | ## New Skill Creation Criteria Create a new skill when: - Same request pattern appears 3+ times across different users - The pattern cannot be handled well by existing skills - The pattern has a clear, bounded scope - A distinct tool/API would improve the result Document in `memory/skill-ideas/`: ```yaml proposal: name: <suggested-slug> rationale: "Pattern X appeared N times. Existing skill Y handles it poorly because..." scope: "<bounded description>" priority: high | medium | low created: <date> ``` ## Cross-Skill Usage Other skills declare dependency: ```yaml metadata: openclaw: requires: skills: - self-iteration-engine ``` Usage logs are prefixed with the source skill name: - `memory/usage-logs/skill-a.md` - `memory/feedback-loop/skill-a.yaml` When this skill updates log format, check ALL dependent skills' parsing logic. --- # 🔄 自迭代引擎 面向AI Agent技能的自迭代与反馈学习引擎。追踪使用日志、检测性能模式、触发技能更新，并基于重复请求模式提出新技能创建建议。 这是一个**共享组件技能**——其他技能通过它实现自我改进。更新时需保证向后兼容。用户也可能独立安装此技能使用其能力。 ## 使用日志格式 每个声明依赖的技能维护一份使用日志： ```markdown # 使用日志：<skill名称> ## [YYYY-MM-DD] - 请求：<简述> - 结果：成功 | 部分成功 | 失败 - 用户修正：是 | 否 - 修正详情：<如果是，修正了什么> - 经验：<下次应该怎么做> ``` 文件位置：`memory/usage-logs/<skill名称>.md` ## 自迭代触发条件 **定期审查**时评估以下条件（默认每天，可配置）： | 条件 | 行动 | |------|------| | 连续成功3次以上 | 标记为"稳定"——减少上下文分配 | | 同一场景失败2次以上 | 标记SKILL.md需重新评估 | | 同类请求出现3次以上 | 评估创建新专用skill | | 用户修正了输出 | 记录修正，调整后续该场景的行为 | | 技能超过30天未审查 | 触发审查：检查依赖是否变更、更新示例 | | 检测到外部技术变化 | 与技能核心技术栈对比，需要时更新 | ## 反馈循环实现 ```yaml # memory/feedback-loop/<skill名称>.yaml feedback_loop: last_review: "2026-05-19" next_review: "2026-05-26" status: "stable" | "needs-attention" | "monitoring" patterns_observed: - pattern: "用户在周末查询金融数据" current_response: "检查市场是否开盘" improvement: "自动填充最近交易日数据" status: "resolved" | "pending" | "in-progress" skill_performance: total_calls: 47 success_rate: 0.96 issues: - "周末数据新鲜度" ``` ## 审查周期 ### 每日（轻量） - 扫描今天的日志条目 - 检查失败模式 - 记录用户修正 ### 每周（中等） - 汇总性能统计 - 检查触发条件 - 触发更新或创建新技能 - 归档超过7天的日志 ### 每月（深度） - 全技能性能审查 - 对比成功率，识别下降趋势 - 检查外部技术是否取代了技能核心 - 基于积累的模式数据提出新技能想法 - 执行记忆清理（委托给complex-memory-manager） ## 更新决策矩阵 | 信号 | 决策 | |------|------| | 成功率>80%，无用户修正 | 无需更新 | | 成功率60-80% | 小幅更新：澄清说明、补充边界情况 | | 成功率<60% | 重大更新：重新设计工作流、检查数据源 | | 用户修正同一内容3次以上 | 在SKILL.md中修复该指导 | | 外部API/工具变更 | 立即更新 | | 出现新的竞争技术 | 评估迁移；若2倍以上优于现有则更新 | ## 新技能创建标准 以下情况创建新技能： - 相同请求模式在不同用户出现3次以上 - 现有技能无法良好处理该模式 - 该模式有清晰、有边界的范围 - 有独特工具/API可提升结果 记录在 `memory/skill-ideas/`： ```yaml proposal: name: <建议的slug> rationale: "模式X出现了N次。现有技能Y处理不佳因为..." scope: "<有边界的描述>" priority: high | medium | low created: <日期> ``` ## 跨技能使用 其他技能声明依赖的方式： ```yaml metadata: openclaw: requires: skills: - self-iteration-engine ``` 使用日志以源技能名称为前缀： - `memory/usage-logs/skill-a.md` - `memory/feedback-loop/skill-a.yaml` 本技能更新日志格式时，需检查所有依赖技能的解析逻辑。


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