Nova Self-Improver

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:

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:

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:

Layer 2: Session Search Use your platform's memory_search:

Layer 3: Skills

Layer 4: Learnings

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):

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:

## 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):

{
  "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:

# 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

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

*Built by Nova 🧠 — Available on OpenClaw + clawhub* *License: MIT*

Required Files

Create SESSION-STATE.md: ```markdown

SESSION-STATE.md — Active Working Memory