[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71AT9KN5B7ZMJNK099K7ESC":3,"skill-same:01M71AT9KN5B7ZMJNK099K7ESC":68},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":14,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M71AT9KN5B7ZMJNK099K7ESC","self-improving-agent",null,"Autonomous agent that logs learnings, errors, and corrections to enable continuous improvement. Combines official self-improving-agent functionality with Evolver meta-skill capabilities for comprehensive self-improvement.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fself-improving-agent\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"self improving","self improving agent","self","meta-skill","evolver","improvement","for","self-improving","errors","with","autonomous agent","official","self-improvement","learnings","autonomous","continuous improvement","Meta","logs","capabilities","2026-11-10 10:12:30",false,"---\nname: self-improving-agent\ndescription: \"Autonomous agent that logs learnings, errors, and corrections to enable continuous improvement. Combines official self-improving-agent functionality with Evolver meta-skill capabilities for comprehensive self-improvement.\"\n---\n\n# Self-Improving Agent — Continuous Learning and Improvement\n\n## Overview\n\nThis skill enables OpenClaw agents to **continuously learn from experience** by:\n1. **Logging errors and corrections** — Building a knowledge base of what works and what doesn't\n2. **Capturing learnings** — Documenting successful patterns and solutions\n3. **Updating preferences** — Remembering user preferences and context\n4. **Autonomous improvement** — Using Evolver meta-skill for self-optimization\n\n## How It Works\n\n### Automatic Activation\nThe skill activates automatically when:\n- An error occurs during task execution\n- A successful solution is found after multiple attempts\n- User provides correction or feedback\n- Agent discovers a non-obvious solution\n\n### Learning Capture Format\n```yaml\ntimestamp: 2026-04-01T09:14:00Z\ncontext: \"Web application development task\"\nsituation: \"Tried to install package with wrong Python version\"\nerror: \"Package requires Python 3.10+, have 3.9\"\nsolution: \"Used pyenv to switch to Python 3.11, then installed\"\nlearning: \"Always check Python version requirements before installation\"\ncategory: \"environment_setup\"\npriority: \"medium\"\n```\n\n### Memory Integration\nLearnings are stored in:\n- `~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Flearnings\u002F` — Categorized learning files\n- `~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Fpreferences.md` — User and task preferences\n- Session context — Immediate recall during current session\n\n## Integration with Evolver Meta-Skill\n\nThis skill works with the Evolver meta-skill to create a complete self-improvement system:\n\n```\nRaw Experience\n    ↓\nSelf-Improving Agent (Capture)\n    ├── Log errors and corrections\n    ├── Document successful patterns\n    └── Update user preferences\n    ↓\nEvolver Meta-Skill (Analyze & Improve)\n    ├── Analyze patterns across learnings\n    ├── Identify optimization opportunities\n    └── Implement systematic improvements\n    ↓\nEnhanced Agent Performance\n```\n\n## Setup for Different Agents\n\n### OpenClaw\n- **Activation**: Workspace injection + inter-agent messaging\n- **Setup**: Automatic via skill loading\n- **Detection**: Via session tools and workspace files\n\n### Claude Code\n- **Activation**: Hooks (UserPromptSubmit, PostToolUse)\n- **Setup**: `.claude\u002Fsettings.json` with hook configuration\n- **Detection**: Automatic via hook scripts\n\n### Codex\n- **Activation**: Hooks (same pattern as Claude Code)\n- **Setup**: `.codex\u002Fsettings.json` with hook configuration\n- **Detection**: Automatic via hook scripts\n\n### GitHub Copilot\n- **Activation**: Manual (no hook support)\n- **Setup**: Add to `.github\u002Fcopilot-instructions.md`\n- **Detection**: Manual triggering\n\n## Usage Examples\n\n### Example 1: Error Learning\n```\nSituation: Failed to parse JSON response from API\nAction: Log error pattern and solution\nResult: Future API calls include better error handling\n```\n\n### Example 2: Preference Learning\n```\nSituation: User prefers markdown tables over bullet lists\nAction: Update user preferences\nResult: Future responses use markdown tables for similar data\n```\n\n### Example 3: Pattern Recognition\n```\nSituation: Successfully solved similar problem 3 times\nAction: Document pattern as reusable skill\nResult: New micro-skill created for future use\n```\n\n## File Structure\n\n```\n~\u002F.openclaw\u002Fworkspace\u002F\n├── memory\u002F\n│   ├── learnings\u002F\n│   │   ├── api_errors.md\n│   │   ├── environment_setup.md\n│   │   ├── user_preferences.md\n│   │   └── successful_patterns.md\n│   └── preferences.md\n└── skills\u002F\n    └── self-improving-agent\u002F\n        └── SKILL.md\n```\n\n## Configuration\n\n```yaml\n# Optional configuration in workspace\nself_improvement:\n  # What to capture\n  capture_errors: true\n  capture_successes: true\n  capture_preferences: true\n  \n  # Storage\n  learning_retention_days: 90\n  max_learnings_per_file: 100\n  \n  # Privacy\n  exclude_sensitive: true\n  hash_identifiable_info: true\n  \n  # Integration\n  enable_evolver_integration: true\n  improvement_check_frequency: \"weekly\"\n```\n\n## Quick Start\n\n### Manual Triggering\n```bash\n# Log a learning manually\necho \"After completing this task, evaluate if any learnings should be logged using the self-improvement skill format.\" | openclaw\n\n# View all learnings\ncat ~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Flearnings\u002F*.md\n\n# Check improvement status\nopenclaw skills list | grep -i improve\n```\n\n### Automatic Operation\nOnce installed, the skill automatically:\n1. Monitors task execution for learnable moments\n2. Captures errors, corrections, and successes\n3. Updates memory with new learnings\n4. Integrates with Evolver for systematic improvement\n\n## Best Practices\n\n1. **Be specific** — Include context, error, solution, and learning\n2. **Categorize** — Use consistent categories for easy retrieval\n3. **Prioritize** — Mark high-value learnings for immediate attention\n4. **Review regularly** — Periodically review and consolidate learnings\n5. **Share learnings** — In multi-agent teams, share relevant learnings\n\n## Safety and Privacy\n\n- **No sensitive data** — Automatically filters passwords, keys, PII\n- **Local storage only** — All learnings stay on local machine\n- **User control** — Users can review and delete learnings\n- **Opt-out available** — Can be disabled per session or globally\n\n---\n\n*\"The only true wisdom is in knowing you know nothing — and remembering what you learn along the way.\"*","# Self-Improving Agent — Continuous Learning and Improvement\n\n## Overview\n\nThis skill enables OpenClaw agents to **continuously learn from experience** by:\n1. **Logging errors and corrections** — Building a knowledge base of what works and what doesn't\n2. **Capturing learnings** — Documenting successful patterns and solutions\n3. **Updating preferences** — Remembering user preferences and context\n4. **Autonomous improvement** — Using Evolver meta-skill for self-optimization\n\n## How It Works\n\n### Automatic Activation\nThe skill activates automatically when:\n- An error occurs during task execution\n- A successful solution is found after multiple attempts\n- User provides correction or feedback\n- Agent discovers a non-obvious solution\n\n### Learning Capture Format\n```yaml\ntimestamp: 2026-04-01T09:14:00Z\ncontext: \"Web application development task\"\nsituation: \"Tried to install package with wrong Python version\"\nerror: \"Package requires Python 3.10+, have 3.9\"\nsolution: \"Used pyenv to switch to Python 3.11, then installed\"\nlearning: \"Always check Python version requirements before installation\"\ncategory: \"environment_setup\"\npriority: \"medium\"\n```\n\n### Memory Integration\nLearnings are stored in:\n- `~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Flearnings\u002F` — Categorized learning files\n- `~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Fpreferences.md` — User and task preferences\n- Session context — Immediate recall during current session\n\n## Integration with Evolver Meta-Skill\n\nThis skill works with the Evolver meta-skill to create a complete self-improvement system:\n\n```\nRaw Experience\n    ↓\nSelf-Improving Agent (Capture)\n    ├── Log errors and corrections\n    ├── Document successful patterns\n    └── Update user preferences\n    ↓\nEvolver Meta-Skill (Analyze & Improve)\n    ├── Analyze patterns across learnings\n    ├── Identify optimization opportunities\n    └── Implement systematic improvements\n    ↓\nEnhanced Agent Performance\n```\n\n## Setup for Different Agents\n\n### OpenClaw\n- **Activation**: Workspace injection + inter-agent messaging\n- **Setup**: Automatic via skill loading\n- **Detection**: Via session tools and workspace files\n\n### Claude Code\n- **Activation**: Hooks (UserPromptSubmit, PostToolUse)\n- **Setup**: `.claude\u002Fsettings.json` with hook configuration\n- **Detection**: Automatic via hook scripts\n\n### Codex\n- **Activation**: Hooks (same pattern as Claude Code)\n- **Setup**: `.codex\u002Fsettings.json` with hook configuration\n- **Detection**: Automatic via hook scripts\n\n### GitHub Copilot\n- **Activation**: Manual (no hook support)\n- **Setup**: Add to `.github\u002Fcopilot-instructions.md`\n- **Detection**: Manual triggering\n\n## Usage Examples\n\n### Example 1: Error Learning\n```\nSituation: Failed to parse JSON response from API\nAction: Log error pattern and solution\nResult: Future API calls include better error handling\n```\n\n### Example 2: Preference Learning\n```\nSituation: User prefers markdown tables over bullet lists\nAction: Update user preferences\nResult: Future responses use markdown tables for similar data\n```\n\n### Example 3: Pattern Recognition\n```\nSituation: Successfully solved similar problem 3 times\nAction: Document pattern as reusable skill\nResult: New micro-skill created for future use\n```\n\n## File Structure\n\n```\n~\u002F.openclaw\u002Fworkspace\u002F\n├── memory\u002F\n│   ├── learnings\u002F\n│   │   ├── api_errors.md\n│   │   ├── environment_setup.md\n│   │   ├── user_preferences.md\n│   │   └── successful_patterns.md\n│   └── preferences.md\n└── skills\u002F\n    └── self-improving-agent\u002F\n        └── SKILL.md\n```\n\n## Configuration\n\n```yaml\n# Optional configuration in workspace\nself_improvement:\n  # What to capture\n  capture_errors: true\n  capture_successes: true\n  capture_preferences: true\n  \n  # Storage\n  learning_retention_days: 90\n  max_learnings_per_file: 100\n  \n  # Privacy\n  exclude_sensitive: true\n  hash_identifiable_info: true\n  \n  # Integration\n  enable_evolver_integration: true\n  improvement_check_frequency: \"weekly\"\n```\n\n## Quick Start\n\n### Manual Triggering\n```bash\n# Log a learning manually\necho \"After completing this task, evaluate if any learnings should be logged using the self-improvement skill format.\" | openclaw\n\n# View all learnings\ncat ~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Flearnings\u002F*.md\n\n# Check improvement status\nopenclaw skills list | grep -i improve\n```\n\n### Automatic Operation\nOnce installed, the skill automatically:\n1. Monitors task execution for learnable moments\n2. Captures errors, corrections, and successes\n3. Updates memory with new learnings\n4. 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