---

name: self-improving-agent description: A universal self-improving agent that learns from ALL skill experiences. Uses multi-memory architecture (semantic + episodic + working) to continuously evolve the codebase. Auto-triggers on skill completion/error with hooks-based self-correction. allowed-tools: Read, Write, Edit, Bash, Grep, Glob, WebSearch metadata: hooks: before_start: - trigger: session-logger mode: auto context: "Start {skill_name}" after_complete: - trigger: create-pr mode: ask_first condition: skills_modified reason: "Submit improvements to repository" - trigger: session-logger mode: auto context: "Self-improvement cycle complete" # Note: on_error intentionally only logs to session to avoid infinite recursion # Self-correction is triggered by other skills (debugger, code-reviewer) completing their work on_error: - trigger: session-logger mode: auto context: "Error captured in {skill_name}"

Self-Improving Agent

"An AI agent that learns from every interaction, accumulating patterns and insights to continuously improve its own capabilities." — Based on 2025 lifelong learning research

Overview

This is a universal self-improvement system that learns from ALL skill experiences, not just PRDs. It implements a complete feedback loop with:

• Multi-Memory Architecture: Semantic + Episodic + Working memory
• Self-Correction: Detects and fixes skill guidance errors
• Self-Validation: Periodically verifies skill accuracy
• Hooks Integration: Auto-triggers on skill events (before_start, after_complete, on_error)
• Evolution Markers: Traceable changes with source attribution

Research-Based Design

Based on 2025 research:

| Research | Key Insight | Application | |----------|-------------|-------------| | SimpleMem | Efficient lifelong memory | Pattern accumulation system | | Multi-Memory Survey | Semantic + Episodic memory | World knowledge + experiences | | Lifelong Learning | Continuous task stream learning | Learn from every skill use | | Evo-Memory | Test-time lifelong learning | Real-time adaptation |

The Self-Improvement Loop

┌─────────────────────────────────────────────────────────────────┐
│                    UNIVERSAL SELF-IMPROVEMENT                    │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   Skill Event → Extract Experience → Abstract Pattern → Update  │
│        │                  │                │         │          │
│        ▼                  ▼                ▼         ▼          │
│   ┌─────────────────────────────────────────────────────┐       │
│   │              MULTI-MEMORY SYSTEM                      │       │
│   ├─────────────────────────────────────────────────────┤       │
│   │  Semantic Memory   │  Episodic Memory  │ Working Memory │  │
│   │  (Patterns/Rules)  │  (Experiences)    │  (Current)     │  │
│   │  memory/semantic/  │  memory/episodic/ │  memory/working/│  │
│   └─────────────────────────────────────────────────────┘       │
│                                                                 │
│   ┌─────────────────────────────────────────────────────┐       │
│   │              FEEDBACK LOOP                            │       │
│   │  User Feedback → Confidence Update → Pattern Adapt   │       │
│   └─────────────────────────────────────────────────────┘       │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

When This Activates

Automatic Triggers (via hooks)

| Event | Trigger | Action | |-------|---------|--------| | before_start | Any skill starts | Log session start | | after_complete | Any skill completes | Extract patterns, update skills | | on_error | Bash returns non-zero exit | Capture error context, trigger self-correction |

Manual Triggers

• User says "自我进化", "self-improve", "从经验中学习"
• User says "分析今天的经验", "总结教训"
• User asks to improve a specific skill

Evolution Priority Matrix

Trigger evolution when new reusable knowledge appears:

| Trigger | Target Skill | Priority | Action | |---------|--------------|----------|--------| | New PRD pattern discovered | prd-planner | High | Add to quality checklist | | Architecture tradeoff clarified | architecting-solutions | High | Add to decision patterns | | API design rule learned | api-designer | High | Update template | | Debugging fix discovered | debugger | High | Add to anti-patterns | | Review checklist gap | code-reviewer | High | Add checklist item | | Perf/security insight | performance-engineer, security-auditor | High | Add to patterns | | UI/UX spec issue | prd-planner, architecting-solutions | High | Add visual spec requirements | | React/state pattern | debugger, refactoring-specialist | Medium | Add to patterns | | Test strategy improvement | test-automator, qa-expert | Medium | Update approach | | CI/deploy fix | deployment-engineer | Medium | Add to troubleshooting |

Multi-Memory Architecture

1. Semantic Memory (memory/semantic-patterns.json)

Stores abstract patterns and rules reusable across contexts:

{
  "patterns": {
    "pattern_id": {
      "id": "pat-2025-01-11-001",
      "name": "Pattern Name",
      "source": "user_feedback|implementation_review|retrospective",
      "confidence": 0.95,
      "applications": 5,
      "created": "2025-01-11",
      "category": "prd_structure|react_patterns|async_patterns|...",
      "pattern": "One-line summary",
      "problem": "What problem does this solve?",
      "solution": { ... },
      "quality_rules": [ ... ],
      "target_skills": [ ... ]
    }
  }
}

2. Episodic Memory (memory/episodic/)

Stores specific experiences and what happened:

memory/episodic/
├── 2025/
│   ├── 2025-01-11-prd-creation.json
│   ├── 2025-01-11-debug-session.json
│   └── 2025-01-12-refactoring.json

{
  "id": "ep-2025-01-11-001",
  "timestamp": "2025-01-11T10:30:00Z",
  "skill": "debugger",
  "situation": "User reported data not refreshing after form submission",
  "root_cause": "Empty callback in onRefresh prop",
  "solution": "Implement actual refresh logic in callback",
  "lesson": "Always verify callbacks are not empty functions",
  "related_pattern": "callback_verification",
  "user_feedback": {
    "rating": 8,
    "comments": "This was exactly the issue"
  }
}

3. Working Memory (memory/working/)

Stores current session context:

memory/working/
├── current_session.json   # Active session data
├── last_error.json        # Error context for self-correction
└── session_end.json       # Session end marker

Self-Improvement Process

Phase 1: Experience Extraction

After any skill completes, extract:

What happened:
  skill_used: {which skill}
  task: {what was being done}
  outcome: {success|partial|failure}

Key Insights:
  what_went_well: [what worked]
  what_went_wrong: [what didn't work]
  root_cause: {underlying issue if applicable}

User Feedback:
  rating: {1-10 if provided}
  comments: {specific feedback}

Phase 2: Pattern Abstraction

Convert experiences to reusable patterns:

| Concrete Experience | Abstract Pattern | Target Skill | |--------------------|------------------|--------------| | "User forgot to save PRD notes" | "Always persist thinking to files" | prd-planner | | "Code review missed SQL injection" | "Add security checklist item" | code-reviewer | | "Callback was empty, didn't work" | "Verify callback implementations" | debugger | | "Net APY position ambiguous" | "UI specs need exact relative positions" | prd-planner |

Abstraction Rules:

If experience_repeats 3+ times:
  pattern_level: critical
  action: Add to skill's "Critical Mistakes" section

If solution_was_effective:
  pattern_level: best_practice
  action: Add to skill's "Best Practices" section

If user_rating >= 7:
  pattern_level: strength
  action: Reinforce this approach

If user_rating <= 4:
  pattern_level: weakness
  action: Add to "What to Avoid" section

Phase 3: Skill Updates

Update the appropriate skill files with evolution markers:

<!-- Evolution: 2025-01-12 | source: ep-2025-01-12-001 | skill: debugger -->

## Pattern Added (2025-01-12)

**Pattern**: Always verify callbacks are not empty functions

**Source**: Episode ep-2025-01-12-001

**Confidence**: 0.95

### Updated Checklist
- [ ] Verify all callbacks have implementations
- [ ] Test callback execution paths

Correction Markers (when fixing wrong guidance):

<!-- Correction: 2025-01-12 | was: "Use callback chain" | reason: caused stale refresh -->

## Corrected Guidance

Use direct state monitoring instead of callback chains:
```typescript
// ✅ Do: Direct state monitoring
const prevPendingCount = usePrevious(pendingCount);

### Phase 4: Memory Consolidation

1. **Update semantic memory** (`memory/semantic-patterns.json`)
2. **Store episodic memory** (`memory/episodic/YYYY-MM-DD-{skill}.json`)
3. **Update pattern confidence** based on applications/feedback
4. **Prune outdated patterns** (low confidence, no recent applications)

## Self-Correction (on_error hook)

Triggered when:
- Bash command returns non-zero exit code
- Tests fail after following skill guidance
- User reports the guidance produced incorrect results

**Process:**

```markdown
## Self-Correction Workflow

1. Detect Error
   - Capture error context from working/last_error.json
   - Identify which skill guidance was followed

2. Verify Root Cause
   - Was the skill guidance incorrect?
   - Was the guidance misinterpreted?
   - Was the guidance incomplete?

3. Apply Correction
   - Update skill file with corrected guidance
   - Add correction marker with reason
   - Update related patterns in semantic memory

4. Validate Fix
   - Test the corrected guidance
   - Ask user to verify

Example:

<!-- Correction: 2025-01-12 | was: "useMemo for claimable ids" | reason: stale data at click time -->

## Self-Correction: Click-Time Computation

**Issue**: Using useMemo for claimable IDs caused stale data
**Fix**: Compute at click time for always-fresh data
**Pattern**: click_time_vs_open_time_computation

Self-Validation

Use the validation template in references/appendix.md when reviewing updates.

Hooks Integration

Wiring Hooks in Claude Code Settings

Add to Claude Code settings (~/.claude/settings.json):

{
  "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"
          }
        ]
      }
    ]
  }
}

Replace ${SKILLS_DIR} with your actual skills path.

Additional References

See references/appendix.md for memory structure, workflow diagrams, metrics, feedback templates, and research links.

Best Practices

DO

• ✅ Learn from EVERY skill interaction
• ✅ Extract patterns at the right abstraction level
• ✅ Update multiple related skills
• ✅ Track confidence and apply counts
• ✅ Ask for user feedback on improvements
• ✅ Use evolution/correction markers for traceability
• ✅ Validate guidance before applying broadly

DON'T

• ❌ Over-generalize from single experiences
• ❌ Update skills without confidence tracking
• ❌ Ignore negative feedback
• ❌ Make changes that break existing functionality
• ❌ Create contradictory patterns
• ❌ Update skills without understanding context

Quick Start

After any skill completes, this agent automatically:

1. Analyzes what happened
2. Extracts patterns and insights
3. Updates relevant skill files
4. Logs to memory for future reference
5. Reports summary to user

References

• SimpleMem: Efficient Lifelong Memory for LLM Agents
• A Survey on the Memory Mechanism of Large Language Model Agents
• Lifelong Learning of LLM based Agents
• Evo-Memory: DeepMind's Benchmark
• Let's Build a Self-Improving AI Agent