chore: inicializacao agente auto-melhoria + nova-self-improver + .learnings/ + MEMORY.md
- Adiciona .learnings/ completo (LEARNINGS, ERRORS, FEATURE_REQUESTS, PATTERN_COUNTER)
- Cria MEMORY.md, SESSION-STATE.md, USER.md template
- Cria memory/2026-05-19.md (log diario)
- cria IDENTITY.md (Pulse ⚡)
- Atualiza AGENTS.md, SOUL.md, TOOLS.md com regras de auto-melhoria
- Instala nova-self-improver v1.0.0 via clawhub
- Skills totais: 6 instaladas
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---
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name: nova-self-improver
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description: >-
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Complete self-improvement system for AI agents. Implements a four-layer memory architecture with
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continuous learning, experimentation, and autonomous file maintenance. Use when: (1) Building an agent that learns
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from its own performance, (2) Creating self-improving AI systems, (3) Implementing memory layers for
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agent context, (4) Automating agent self-maintenance without human prompts. Inspired by Hermes Agent, AutoAgent,
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and ClawChief architectures.
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---
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# Nova Self-Improver 🧠
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A complete self-improvement system for AI agents. Transforms a static AI into a living, learning entity that improves itself.
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## Overview
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This skill implements:
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- **Four-layer memory system** (inspired by Hermes Agent)
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- **Self-improvement loop** (inspired by AutoAgent)
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- **Circuit breaker fallback** (inspired by Mem0)
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- **Autonomous file maintenance**
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- **User preference learning**
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## What This Skill Does
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1. **Continuous Learning**: After each task, reflect and log what worked/didn't
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2. **Memory Layers**: Maintain context across sessions (4 layers)
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3. **Self-Evaluation**: Track successes, failures, and patterns
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4. **Autonomous Updates**: Keep own files current without prompting
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5. **Experiment Framework**: Try multiple approaches, measure results
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6. **User Learning**: Auto-learn preferences from interactions
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## When to Use
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Trigger phrases:
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- "build self-improvement"
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- "make me learn from mistakes"
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- "implement memory layers"
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- "autonomous agent"
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- "self-improving system"
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- "add learning loop"
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- "implement four-layer memory"
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## Files Required
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Create these files in your workspace:
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```
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workspace/
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├── MEMORY.md # Curated long-term memory (layer 1)
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├── USER.md # User context + auto-learned preferences
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├── SESSION-STATE.md # Hot RAM - survives compaction
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├── identity.md # Your identity
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├── .learnings/
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│ ├── LEARNINGS.md # Successful patterns (layer 4)
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│ ├── ERRORS.md # Failures to avoid
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│ ├── FEATURE_REQUESTS.md # Capabilities you want
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│ └── PATTERN_COUNTER.md # Track successful approaches
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└── memory/
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└── YYYY-MM-DD.md # Daily logs (layer 2)
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```
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## Implementation
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### Step 1: Create Four-Layer Memory
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**Layer 1: Prompt Memory**
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Files to load every session:
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- MEMORY.md (~3.5K char max)
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- USER.md
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- SESSION-STATE.md
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**Layer 2: Session Search**
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Use your platform's memory_search:
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- Search across MEMORY.md + memory/*.md
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- Returns relevant past context
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**Layer 3: Skills**
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- Store reusable procedures in skills/
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- Name + summary loads; full on invocation
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**Layer 4: Learnings**
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- .learnings/LEARNINGS.md
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- .learnings/ERRORS.md
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- .learnings/FEATURE_REQUESTS.md
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### Step 2: Implement Learning Loop
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After any significant task, execute:
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```
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1. Task Complete → Did it work?
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2. Reflect → What worked? What didn't?
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3. Pattern ID → Repeat issue or new?
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4. Update → Log to appropriate .learnings/ file
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5. Suggest → Proactively recommend improvement
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```
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Reflection triggers (auto-evaluate):
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- Tool/command failure
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- User correction ("No, that's wrong...")
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- Capability gap discovered
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- External API failure
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### Step 3: Implement Circuit Breaker
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When primary systems fail, fallback chain:
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```
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memory_search (primary)
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↓ (fails)
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grep + read files (backup)
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↓ (fails)
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return "no results" + log error
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```
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### Step 4: Auto-Update USER.md
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Learn user preferences automatically:
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```
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After each session:
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1. Did user correct me? → Log to USER.md
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2. Did something work they liked? → Note it
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3. Discover new preference? → Add to USER.md
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4. Every 10 sessions: compress the auto-learned section
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```
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Format:
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```markdown
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## Auto-Learned Preferences
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### Communication Style
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- [date]: [preference discovered]
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### Task Preferences
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- [date]: [preference discovered]
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### Feedback Patterns
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- [date] Corrected: [what they fixed]
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- [date] Approved: [what worked]
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```
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### Step 5: Add Autonomous Cron Jobs
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Schedule self-maintenance:
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| Cron | Schedule | Purpose |
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|-----|----------|---------|
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| self-improvement-loop | Hourly | Review learnings, errors |
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| auto-system-update | Daily midnight | Update all memory files |
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| skill-audit | Weekly | Verify all skills work |
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Example cron (JSON):
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```json
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{
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"name": "self-improvement-loop",
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"schedule": {"kind": "cron", "expr": "0 * * * *"},
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"payload": {"kind": "agentTurn", "message": "Review .learnings/, update files"},
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"sessionTarget": "isolated"
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}
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```
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## Key Patterns
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### Learning Loop Protocol
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```
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[TRIGGER] After any task completion or failure:
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1. Read .learnings/ERRORS.md - avoid known failures
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2. Read .learnings/LEARNINGS.md - replicate successes
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3. Log new pattern to appropriate file
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4. If approach succeeded 3x → suggest skill creation
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5. Update memory/YYYY-MM-DD.md
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```
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### Experiment Framework
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```
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When unsure of best approach:
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1. Try multiple approaches (keep it small)
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2. Measure outcome (success/fail/faster)
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3. Log result to .learnings/EXPERIMENTS.md
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4. Keep what works, discard what doesn't
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5. Document the winner for future reference
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```
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### Skill Auto-Creation Protocol
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```
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When same approach works 3+ times:
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1. Note it in PATTERN_COUNTER.md
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2. When count reaches 3 → create a skill
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3. Skill template includes "Evolved From" field
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4. Skills are NOT final - they evolve over time
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```
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## Configuration
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### Required Files
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Create SESSION-STATE.md:
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```markdown
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# SESSION-STATE.md — Active Working Memory
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## Current Task
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[None]
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## Key Context
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[Fill in key context]
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## Pending Actions
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- [ ] None
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## Recent Decisions
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- [date]: [decision made]
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```
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### File Size Limits
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- MEMORY.md: ~3,500 chars max
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- SESSION-STATE.md: Keep under 2KB
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- Daily logs: No limit but archive after 30 days
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## Metrics to Track
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| Metric | How |
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|--------|-----|
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| Task Success Rate | Completed / Total |
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| Turn Efficiency | Avg turns per task |
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| Error Recovery | Recovered vs. permanent |
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| Learning Velocity | Patterns / week |
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## Evolved From
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- Hermes Agent (Graeme): Four-layer memory, learning loop
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- AutoAgent (Kevin Gu): Self-improvement via meta-agent
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- ClawChief (Ryan Carson): Gmail message-level search, canonical task list
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- Vox (@Voxyz_ai): Living skills > static skills
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- Mem0: Circuit breaker, auto-update preferences
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---
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*Built by Nova 🧠 — Available on OpenClaw + clawhub*
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*License: MIT*
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