Self-Improving + Proactive Agent

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tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task."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

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
想要更強大的技能外掛，就來小蔥技能站7w4.net看看吧。

Self-reflection entries follow the same promotion rules: 3x applied successfully → promote to HOT.

Quick Queries

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

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

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 <slug> 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

這個技能能讓 AI 越用越懂你，主動記住你的偏好和糾正。質量很好：文件清晰、設定有指引、安全考慮周到。優點是會自動從錯誤中學習、記憶分層次管理、使用者可以隨時清除資料。缺點是初始設定稍複雜、需要修改好幾個配置檔案。總體適合希望 AI 長期適應自己工作習慣的使用者。