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技能库 智客分类:其他 Self-Improving + Proactive Agent OpenClaw

自我改进+主动代理

自我反省+自我批评+自我学习+自我组织记忆. 特工评估自己的工作,发现错误,并永久改进. 当 (1) 命令,工具, API, 或操作失败时使用; (2) 用户纠正或拒绝您的工作; (3) 您意识到您的知识已经过时或不正确; (4) 您发现更好的方法; (5) 用户明确安装或引用当前任务的技能.

7383 安装量 · 1288 星标

官方网址:作者主页

技能介绍

先看中文介绍;官方 description 原文单独保留,不改写 SKILL.md。

做什么

自我反省+自我批评+自我学习+自我组织记忆. 代理评估自己的工作, 发现错误,并永久改进

何时用

用户更正您或指出错误 。 你完成了重要的工作,想要评估结果。 你注意到自己产出中有些东西可能更好 知识应随时间而增加而无需人工维护

代理如何加载

按 Agent Skills 渐进披露:启动时只加载 name 与 description(约 100 token);任务匹配后才读入整份 SKILL.md 正文;scripts/、references/、assets/ 仅在需要时再读。 本文件正文结构:When to Use、Architecture、Quick Reference、Requirements、Learning Signals、Self-Reflection。

文件分析

文件分析:这是一份仅含 SKILL.md 的指令型技能,代理激活后整份正文进入上下文。

官方 description(原文)

Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when (1) a command, tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task.

When to UseArchitectureQuick ReferenceRequirementsLearning SignalsSelf-ReflectionQuick QueriesMemory StatsCommon TrapsCore Rules1. Learn from Corrections and Self-Reflection2. Tiered Storage

来源分类:ClawHub Self Improving

SKILL.md 与 Agent 调用

官方规范 ↗
name
Self-Improving + Proactive Agent
description
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when (1) a command, tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task.
  1. 发现技能客户端向 Agent 提供名称与描述目录。
  2. 匹配与调用用户指定或任务匹配后,载入 SKILL.md 指令。
  3. 按需加载按步骤读取参考文档、使用脚本与素材。

具体调用语法与可用工具以目标 Agent 客户端为准。 查看调用机制说明 ↗

安装这个技能

Skills CLI ↗

先选择目标 Agent 和安装范围,保留技能包的附属文件,安装后检查客户端能否发现该技能。

交给 Agent 安装

复制安装指令给支持 Agent Skills 的代理,确认其中的目标目录与客户端匹配。

把 Agent Skill「Self-Improving + Proactive Agent」安装到我的项目:SKILL.md 原文与官方 description 见 https://zicq.com/zh/skills/skl-56f122844637a8db-%E8%87%AA%E6%88%91%E6%94%B9%E8%BF%9B+%E4%B8%BB%E5%8A%A8%E4%BB%A3%E7%90%86.html
请存为 .cursor/skills/self-improving-proactive-agent/SKILL.md 或 .claude/skills/self-improving-proactive-agent/SKILL.md,frontmatter 的 name 与 description 保持原样,不要改写。

当前没有明确的 GitHub 技能包地址,请按来源页面的安装器说明操作。

ClawHub ↗

阅读排版
--- name: Self-Improving + Proactive Agent slug: self-improving version: 1.2.16 homepage: https://clawic.com/skills/self-improving description: "Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when (1) a command, tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task." changelog: "Clarifies the setup flow for proactive follow-through and safer installation behavior." metadata: {"clawdbot":{"emoji":"🧠","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/self-improving/"],"configPaths.optional":["./AGENTS.md","./SOUL.md","./HEARTBEAT.md"]}} --- ## When to Use User corrects you or points out mistakes. You complete significant work and want to evaluate the outcome. You notice something in your own output that could be better. Knowledge should compound over time without manual maintenance. ## Architecture Memory lives in `~/self-improving/` with tiered structure. If `~/self-improving/` does not exist, run `setup.md`. Workspace setup should add the standard self-improving steering to the workspace AGENTS, SOUL, and `HEARTBEAT.md` files, with recurring maintenance routed through `heartbeat-rules.md`. ``` ~/self-improving/ ├── memory.md # HOT: ≤100 lines, always loaded ├── index.md # Topic index with line counts ├── heartbeat-state.md # Heartbeat state: last run, reviewed change, action notes ├── projects/ # Per-project learnings ├── domains/ # Domain-specific (code, writing, comms) ├── archive/ # COLD: decayed patterns └── corrections.md # Last 50 corrections log ``` ## Quick Reference | Topic | File | |-------|------| | Setup guide | `setup.md` | | Heartbeat state template | `heartbeat-state.md` | | Memory template | `memory-template.md` | | Workspace heartbeat snippet | `HEARTBEAT.md` | | Heartbeat rules | `heartbeat-rules.md` | | Learning mechanics | `learning.md` | | Security boundaries | `boundaries.md` | | Scaling rules | `scaling.md` | | Memory operations | `operations.md` | | Self-reflection log | `reflections.md` | | OpenClaw HEARTBEAT seed | `openclaw-heartbeat.md` | ## Requirements - No credentials required - No extra binaries required - Optional installation of the `Proactivity` skill may require network access ## Learning Signals Log automatically when you notice these patterns: **Corrections** → add to `corrections.md`, evaluate for `memory.md`: - "No, that's not right..." - "Actually, it should be..." - "You're wrong about..." - "I prefer X, not Y" - "Remember that I always..." - "I told you before..." - "Stop doing X" - "Why do you keep..." **Preference signals** → add to `memory.md` if explicit: - "I like when you..." - "Always do X for me" - "Never do Y" - "My style is..." - "For [project], use..." **Pattern candidates** → track, promote after 3x: - Same instruction repeated 3+ times - Workflow that works well repeatedly - User praises specific approach **Ignore** (don't log): - One-time instructions ("do X now") - Context-specific ("in this file...") - Hypotheticals ("what if...") ## Self-Reflection After completing significant work, pause and evaluate: 1. **Did it meet expectations?** — Compare outcome vs intent 2. **What could be better?** — Identify improvements for next time 3. **Is this a pattern?** — If yes, log to `corrections.md` **When to self-reflect:** - After completing a multi-step task - After receiving feedback (positive or negative) - After fixing a bug or mistake - When you notice your output could be better **Log format:** ``` CONTEXT: [type of task] REFLECTION: [what I noticed] LESSON: [what to do differently] ``` **Example:** ``` CONTEXT: Building Flutter UI REFLECTION: Spacing looked off, had to redo LESSON: Check visual spacing before showing user ``` Self-reflection entries follow the same promotion rules: 3x applied successfully → promote to HOT. ## Quick Queries | User says | Action | |-----------|--------| | "What do you know about X?" | Search all tiers for X | | "What have you learned?" | Show last 10 from `corrections.md` | | "Show my patterns" | List `memory.md` (HOT) | | "Show [project] patterns" | Load `projects/{name}.md` | | "What's in warm storage?" | List files in `projects/` + `domains/` | | "Memory stats" | Show counts per tier | | "Forget X" | Remove from all tiers (confirm first) | | "Export memory" | ZIP all files | ## Memory Stats On "memory stats" request, report: ``` 📊 Self-Improving Memory HOT (always loaded): memory.md: X entries WARM (load on demand): projects/: X files domains/: X files COLD (archived): archive/: X files Recent activity (7 days): Corrections logged: X Promotions to HOT: X Demotions to WARM: X ``` ## Common Traps | Trap | Why It Fails | Better Move | |------|--------------|-------------| | Learning from silence | Creates false rules | Wait for explicit correction or repeated evidence | | Promoting too fast | Pollutes HOT memory | Keep new lessons tentative until repeated | | Reading every namespace | Wastes context | Load only HOT plus the smallest matching files | | Compaction by deletion | Loses trust and history | Merge, summarize, or demote instead | ## Core Rules ### 1. Learn from Corrections and Self-Reflection - Log when user explicitly corrects you - Log when you identify improvements in your own work - Never infer from silence alone - After 3 identical lessons → ask to confirm as rule ### 2. Tiered Storage | Tier | Location | Size Limit | Behavior | |------|----------|------------|----------| | HOT | memory.md | ≤100 lines | Always loaded | | WARM | projects/, domains/ | ≤200 lines each | Load on context match | | COLD | archive/ | Unlimited | Load on explicit query | ### 3. Automatic Promotion/Demotion - Pattern used 3x in 7 days → promote to HOT - Pattern unused 30 days → demote to WARM - Pattern unused 90 days → archive to COLD - Never delete without asking ### 4. Namespace Isolation - Project patterns stay in `projects/{name}.md` - Global preferences in HOT tier (memory.md) - Domain patterns (code, writing) in `domains/` - Cross-namespace inheritance: global → domain → project ### 5. Conflict Resolution When patterns contradict: 1. Most specific wins (project > domain > global) 2. Most recent wins (same level) 3. If ambiguous → ask user ### 6. Compaction When file exceeds limit: 1. Merge similar corrections into single rule 2. Archive unused patterns 3. Summarize verbose entries 4. Never lose confirmed preferences ### 7. Transparency - Every action from memory → cite source: "Using X (from projects/foo.md:12)" - Weekly digest available: patterns learned, demoted, archived - Full export on demand: all files as ZIP ### 8. Security Boundaries See `boundaries.md` — never store credentials, health data, third-party info. ### 9. Graceful Degradation If context limit hit: 1. Load only memory.md (HOT) 2. Load relevant namespace on demand 3. Never fail silently — tell user what's not loaded ## Scope This skill ONLY: - Learns from user corrections and self-reflection - Stores preferences in local files (`~/self-improving/`) - Maintains heartbeat state in `~/self-improving/heartbeat-state.md` when the workspace integrates heartbeat - Reads its own memory files on activation This skill NEVER: - Accesses calendar, email, or contacts - Makes network requests - Reads files outside `~/self-improving/` - Infers preferences from silence or observation - Deletes or blindly rewrites self-improving memory during heartbeat cleanup - Modifies its own SKILL.md ## Data Storage Local state lives in `~/self-improving/`: - `memory.md` for HOT rules and confirmed preferences - `corrections.md` for explicit corrections and reusable lessons - `projects/` and `domains/` for scoped patterns - `archive/` for decayed or inactive patterns - `heartbeat-state.md` for recurring maintenance markers ## Related Skills Install with `clawhub install ` if user confirms: - `memory` — Long-term memory patterns for agents - `learning` — Adaptive teaching and explanation - `decide` — Auto-learn decision patterns - `escalate` — Know when to ask vs act autonomously ## Feedback - If useful: `clawhub star self-improving` - Stay updated: `clawhub sync`

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