自优化代理
Self-Improving Agent Skill
by amdf01-debug
安装
claude skill add --url github.com/openclaw/skills/tree/main/skills/amdf01-debug/sw-self-improving-agent文档
Trigger
Build agents that learn from corrections and get better over time.
Trigger phrases: "self-improving agent", "agent learns", "correction loop", "agent keeps making mistakes", "teach my agent"
The Correction Loop
User corrects agent → Agent logs correction to RULES.md →
Next session, agent reads RULES.md → Agent avoids the mistake →
Over time, RULES.md becomes a refined operating manual
Implementation
RULES.md Structure
# RULES.md — Self-Improving Operating Rules
## Communication
- [2026-03-15] Never use "I hope this helps" — just end the message
- [2026-03-18] When drafting emails, provide ONLY the email text — no commentary
## Operations
- [2026-03-16] Check calendar BEFORE suggesting meeting times
- [2026-03-20] When referencing a project, include status from projects/ folder
## People
- [2026-03-17] Client X prefers formal communication
- [2026-03-19] Always CC studio manager on client emails unless told otherwise
Rules for Rules
- Date-stamp every rule
- One rule per line — atomic, independently useful
- Max ~150 rules (beyond this, models start losing adherence)
- Review monthly: remove stale rules, merge duplicates
- If two rules contradict, the newer one wins
- Promote patterns (not incidents) — "always check X before Y" > "that one time X broke"
AGENTS.md Integration
Add to your AGENTS.md:
## Self-Improvement
After ANY correction from the user:
1. Log the correction pattern to RULES.md with date
2. Identify the general rule (not just the specific instance)
3. Check if a similar rule already exists — update rather than duplicate
4. Silently scan RULES.md every ~10 interactions for contradictions
Metrics
- Track correction frequency over time (should decrease)
- Track RULES.md size (should grow, then plateau)
- Track unique vs repeat corrections (repeats should approach zero)
相关 Skills
Claude接口
by anthropics
面向接入 Claude API、Anthropic SDK 或 Agent SDK 的开发场景,自动识别项目语言并给出对应示例与默认配置,快速搭建 LLM 应用。
✎ 想把Claude能力接进应用或智能体,用claude-api上手快、兼容Anthropic与Agent SDK,集成路径清晰又省心
提示工程专家
by alirezarezvani
覆盖Prompt优化、Few-shot设计、结构化输出、RAG评测与Agent工作流编排,适合分析token成本、评估LLM输出质量,并搭建可落地的AI智能体系统。
✎ 把提示优化、LLM评测到RAG与智能体设计串成一套方法,适合想系统提升AI开发效率的人。
智能体流程设计
by alirezarezvani
面向生产级多 Agent 编排,梳理顺序、并行、分层、事件驱动、共识五种工作流设计,覆盖 handoff、状态管理、容错重试、上下文预算与成本优化,适合搭建复杂 AI 协作系统。
✎ 帮你把多智能体流程设计、编排和自动化统一起来,复杂工作流也能更稳地落地,适合追求强控制力的团队。
相关 MCP 服务
顺序思维
编辑精选by Anthropic
Sequential Thinking 是让 AI 通过动态思维链解决复杂问题的参考服务器。
✎ 这个服务器展示了如何让 Claude 像人类一样逐步推理,适合开发者学习 MCP 的思维链实现。但注意它只是个参考示例,别指望直接用在生产环境里。
知识图谱记忆
编辑精选by Anthropic
Memory 是一个基于本地知识图谱的持久化记忆系统,让 AI 记住长期上下文。
✎ 帮 AI 和智能体补上“记不住”的短板,用本地知识图谱沉淀长期上下文,连续对话更聪明,数据也更可控。
PraisonAI
编辑精选by mervinpraison
PraisonAI 是一个支持自反思和多 LLM 的低代码 AI 智能体框架。
✎ 如果你需要快速搭建一个能 24/7 运行的 AI 智能体团队来处理复杂任务(比如自动研究或代码生成),PraisonAI 的低代码设计和多平台集成(如 Telegram)让它上手极快。但作为非官方项目,它的生态成熟度可能不如 LangChain 等主流框架,适合愿意尝鲜的开发者。