Skill: j3m2b/nova-self-improver (version 1.0.0)

--- name: nova-self-improver description: >- Complete self-improvement system for AI agents. Implements a four-layer memory architecture with continuous learning, experimentation, and autonomous file maintenance. Use when: (1) Building an agent that learns from its own performance, (2) Creating self-improving AI systems, (3) Implementing memory layers for agent context, (4) Automating agent self-maintenance without human prompts. Inspired by Hermes Agent, AutoAgent, and ClawChief architectures. --- # Nova Self-Improver 🧠 A complete self-improvement system for AI agents. Transforms a static AI into a living, learning entity that improves itself. ## Overview This skill implements: - **Four-layer memory system** (inspired by Hermes Agent) - **Self-improvement loop** (inspired by AutoAgent) - **Circuit breaker fallback** (inspired by Mem0) - **Autonomous file maintenance** - **User preference learning** ## What This Skill Does 1. **Continuous Learning**: After each task, reflect and log what worked/didn't 2. **Memory Layers**: Maintain context across sessions (4 layers) 3. **Self-Evaluation**: Track successes, failures, and patterns 4. **Autonomous Updates**: Keep own files current without prompting 5. **Experiment Framework**: Try multiple approaches, measure results 6. **User Learning**: Auto-learn preferences from interactions ## When to Use Trigger phrases: - "build self-improvement" - "make me learn from mistakes" - "implement memory layers" - "autonomous agent" - "self-improving system" - "add learning loop" - "implement four-layer memory" ## Files Required Create these files in your workspace: ``` workspace/ ├── MEMORY.md # Curated long-term memory (layer 1) ├── USER.md # User context + auto-learned preferences ├── SESSION-STATE.md # Hot RAM - survives compaction ├── identity.md # Your identity ├── .learnings/ │ ├── LEARNINGS.md # Successful patterns (layer 4) │ ├── ERRORS.md # Failures to avoid │ ├── FEATURE_REQUESTS.md # Capabilities you want │ └── PATTERN_COUNTER.md # Track successful approaches └── memory/ └── YYYY-MM-DD.md # Daily logs (layer 2) ``` ## Implementation ### Step 1: Create Four-Layer Memory **Layer 1: Prompt Memory** Files to load every session: - MEMORY.md (~3.5K char max) - USER.md - SESSION-STATE.md **Layer 2: Session Search** Use your platform's memory_search: - Search across MEMORY.md + memory/*.md - Returns relevant past context **Layer 3: Skills** - Store reusable procedures in skills/ - Name + summary loads; full on invocation **Layer 4: Learnings** - .learnings/LEARNINGS.md - .learnings/ERRORS.md - .learnings/FEATURE_REQUESTS.md ### Step 2: Implement Learning Loop After any significant task, execute: ``` 1. Task Complete → Did it work? 2. Reflect → What worked? What didn't? 3. Pattern ID → Repeat issue or new? 4. Update → Log to appropriate .learnings/ file 5. Suggest → Proactively recommend improvement ``` Reflection triggers (auto-evaluate): - Tool/command failure - User correction ("No, that's wrong...") - Capability gap discovered - External API failure ### Step 3: Implement Circuit Breaker When primary systems fail, fallback chain: ``` memory_search (primary) ↓ (fails) grep + read files (backup) ↓ (fails) return "no results" + log error ``` ### Step 4: Auto-Update USER.md Learn user preferences automatically: ``` After each session: 1. Did user correct me? → Log to USER.md 2. Did something work they liked? → Note it 3. Discover new preference? → Add to USER.md 4. Every 10 sessions: compress the auto-learned section ``` Format: ```markdown ## Auto-Learned Preferences ### Communication Style - [date]: [preference discovered] ### Task Preferences - [date]: [preference discovered] ### Feedback Patterns - [date] Corrected: [what they fixed] - [date] Approved: [what worked] ``` ### Step 5: Add Autonomous Cron Jobs Schedule self-maintenance: | Cron | Schedule | Purpose | |-----|----------|---------| | self-improvement-loop | Hourly | Review learnings, errors | | auto-system-update | Daily midnight | Update all memory files | | skill-audit | Weekly | Verify all skills work | Example cron (JSON): ```json { "name": "self-improvement-loop", "schedule": {"kind": "cron", "expr": "0 * * * *"}, "payload": {"kind": "agentTurn", "message": "Review .learnings/, update files"}, "sessionTarget": "isolated" } ``` ## Key Patterns ### Learning Loop Protocol ``` [TRIGGER] After any task completion or failure: 1. Read .learnings/ERRORS.md - avoid known failures 2. Read .learnings/LEARNINGS.md - replicate successes 3. Log new pattern to appropriate file 4. If approach succeeded 3x → suggest skill creation 5. Update memory/YYYY-MM-DD.md ``` ### Experiment Framework ``` When unsure of best approach: 1. Try multiple approaches (keep it small) 2. Measure outcome (success/fail/faster) 3. Log result to .learnings/EXPERIMENTS.md 4. Keep what works, discard what doesn't 5. Document the winner for future reference ``` ### Skill Auto-Creation Protocol ``` When same approach works 3+ times: 1. Note it in PATTERN_COUNTER.md 2. When count reaches 3 → create a skill 3. Skill template includes "Evolved From" field 4. Skills are NOT final - they evolve over time ``` ## Configuration ### Required Files Create SESSION-STATE.md: ```markdown # SESSION-STATE.md — Active Working Memory ## Current Task [None] ## Key Context [Fill in key context] ## Pending Actions - [ ] None ## Recent Decisions - [date]: [decision made] ``` ### File Size Limits - MEMORY.md: ~3,500 chars max - SESSION-STATE.md: Keep under 2KB - Daily logs: No limit but archive after 30 days ## Metrics to Track | Metric | How | |--------|-----| | Task Success Rate | Completed / Total | | Turn Efficiency | Avg turns per task | | Error Recovery | Recovered vs. permanent | | Learning Velocity | Patterns / week | ## Evolved From - Hermes Agent (Graeme): Four-layer memory, learning loop - AutoAgent (Kevin Gu): Self-improvement via meta-agent - ClawChief (Ryan Carson): Gmail message-level search, canonical task list - Vox (@Voxyz_ai): Living skills > static skills - Mem0: Circuit breaker, auto-update preferences --- *Built by Nova 🧠 — Available on OpenClaw + clawhub* *License: MIT*

path: _meta.json
content: {
  "name": "nova-self-improver",
  "version": "1.0.0",
  "description": "Complete self-improvement system for AI agents with four-layer memory and autonomous learning",
  "author": "Nova 🧠",
  "keywords": ["self-improvement", "memory", "learning-loop", "autonomous", "four-layer-memory", "ai-agent"],
  "license": "MIT",
  "platform": ["openclaw"],
  "dependencies": [],
  "triggers": [
    "build self-improvement",
    "make me learn from mistakes", 
    "implement memory layers",
    "autonomous agent",
    "self-improving system",
    "add learning loop",
    "implement four-layer memory"
  ]
}

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)