🔄 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:

# Usage Log: \x3Cskill-name>
## [YYYY-MM-DD]
- Request: \x3Cbrief description>
- Outcome: success | partial | fail
- User corrected: yes | no
- Correction detail: \x3Cif yes, what was corrected>
- Lesson: \x3Cwhat to do differently next time>

File location: memory/usage-logs/\x3Cskill-name>.md

Self-Iteration Triggers

Evaluate these conditions during each periodic review (default daily, configurable):

Feedback Loop Implementation

# memory/feedback-loop/\x3Cskill-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

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/:

proposal:
  name: \x3Csuggested-slug>
  rationale: "Pattern X appeared N times. Existing skill Y handles it poorly because..."
  scope: "\x3Cbounded description>"
  priority: high | medium | low
  created: \x3Cdate>

Cross-Skill Usage

Other skills declare dependency:

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：

# 使用日志：\x3Cskill名称>
## [YYYY-MM-DD]
- 请求：\x3C简述>
- 结果：成功 | 部分成功 | 失败
- 用户修正：是 | 否
- 修正详情：\x3C如果是，修正了什么>
- 经验：\x3C下次应该怎么做>

文件位置：memory/usage-logs/\x3Cskill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）：

反馈循环实现

# memory/feedback-loop/\x3Cskill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

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/：

proposal:
  name: \x3C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: "\x3C有边界的描述>"
  priority: high | medium | low
  created: \x3C日期>

跨技能使用

其他技能声明依赖的方式：

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。