Self-Improving Agent

A universal self-improvement system that learns from ALL skill experiences and continuously updates the codebase.

Overview

This agent learns from every skill interaction to achieve true lifelong learning. It implements a complete feedback loop with multi-memory architecture, self-correction, and evolution markers.

Key Features

• Multi-Memory Architecture: Semantic + Episodic + Working memory
• Universal Learning: Learns from ALL skills, not just PRDs
• Pattern Extraction: Converts experiences into reusable patterns
• Self-Correction: Fixes skill guidance when errors occur
• Self-Validation: Periodically verifies skill accuracy
• Automatic Updates: Updates related skills based on learned patterns
• Confidence Tracking: Measures pattern reliability over time
• Human-in-the-Loop: Collects feedback to validate improvements

Memory System

~/.claude/memory/
├── semantic/       # Patterns, rules, best practices
├── episodic/       # Specific experiences and episodes
└── working/        # Current session context

How It Works

Any Skill Completes
        ↓
Extract Experience → Identify Patterns → Update Skills → Consolidate Memory
        ↓                     ↓                  ↓              ↓
   What happened?    What can we reuse?   Which skills?    Track metrics

Installation

ln -s ~/path/to/agent-playbook/skills/self-improving-agent ~/.claude/skills/self-improving-agent

Hooks (Optional)

Wire hooks to capture errors and session-end signals:

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash|Write|Edit",
        "hooks": [
          { "type": "command", "command": "bash ${SKILLS_DIR}/self-improving-agent/hooks/pre-tool.sh \"$TOOL_NAME\" \"$TOOL_INPUT\"" }
        ]
      }
    ],
    "PostToolUse": [
      {
        "matcher": "Bash",
        "hooks": [
          { "type": "command", "command": "bash ${SKILLS_DIR}/self-improving-agent/hooks/post-bash.sh \"$TOOL_OUTPUT\" \"$EXIT_CODE\"" }
        ]
      }
    ],
    "Stop": [
      {
        "matcher": "",
        "hooks": [
          { "type": "command", "command": "bash ${SKILLS_DIR}/self-improving-agent/hooks/session-end.sh" }
        ]
      }
    ]
  }
}

Triggering

Automatic

After ANY skill completes:

• prd-planner
• code-reviewer
• debugger
• refactoring-specialist
• etc.

Manual

"自我进化"
"self-improve"
"分析今天的经验"
"总结这次教训"

Example Learning

Episode

Skill: debugger
Situation: Form submission doesn't refresh data
Root Cause: Empty callback function
Pattern: Always verify callbacks have implementations
Confidence: 0.95 → Updates: debugger, prd-implementation-precheck

Skill Update

## Auto-Update (2025-01-11)

### Pattern Added
**Callback Verification**: Always verify that callback functions
passed as props are not empty and actually execute logic.

**Source**: Episode ep-2025-01-11-003 (3 occurrences)
**Action**: Added to debugger checklist

Research Basis

• [SimpleMem: Efficient Lifelong Memory](https://arxiv.org/html/2601.02553v1)
• [ACM Memory Mechanisms Survey](https://dl.acm.org/doi/10.1145/3748302)
• [Lifelong Learning of LLM Agents](https://arxiv.org/html/2501.07278v1)

Templates

Reusable templates live in skills/self-improving-agent/templates:

• pattern-template.md
• correction-template.md
• validation-template.md

License

MIT