/self-improving-agent

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](https://arxiv.org/html/2601.02553v1) | Efficient lifelong memory | Pattern accumulation system | | [Multi-Memory Survey](https://dl.acm.org/doi/10.1145/3748302) | Semantic + Episodic memory | World knowledge + experiences | | [Lifelong Learning](https://arxiv.org/html/2501.07278v1) | Continuous task stream learning | Learn from every skill use | | [Evo-Memory](https://shothota.medium.com/evo-memory-deepminds-new-benchmark) | 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 "self-evolve", "self-improve", "learn from experience"
• User says "analyze today's experiences", "summarize lessons learned"
• 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":