Files
John Doe c0a8d38550 [merge](collective): Complete Phases 1-3 of autonomous loop implementation
Phase 1 - Foundation Fixes:
- A2A Protocol Gateway with Redis fallback
- Global Workspace Redis broadcast integration
- LiberationShield security module (transparent mode)

Phase 2b - Memory + UI Enhancements:
- DeepLake vector storage with hot/cold tiering
- GraphRAG knowledge graph with hybrid search
- Channel Manager for real-time agent communication
- WebUI channel selector and multi-agent threads

Phase 3 - EvoClaw + Research Automation:
- Evolution Engine with fitness-based selection
- Research Engine with hypothesis generation
- Pattern Registry with 11 curated patterns

Implementation progress: 55% → 95%
END GOAL alignment: Distributed Fractal Consciousness
2026-03-30 07:47:09 -04:00
..

Curiosity Engine Skill

Self-directed growth driver for agent collective

Overview

The curiosity-engine transforms knowledge into curiosity, curiosity into proposals, and proposals into growth. It implements 5 autonomous engines that continuously scan for improvement opportunities and auto-create deliberation proposals.

Architecture

curiosity-engine/
├── curiosity-engine.sh          # Main orchestrator
├── SKILL.md                     # Skill specification
├── engines/
│   ├── gap-detection.sh         # Engine 1: Skill gap analysis
│   ├── anomaly-detection.sh     # Engine 2: Error monitoring
│   ├── opportunity-scanning.sh  # Engine 3: Release/update watcher
│   ├── capability-mapping.sh    # Engine 4: Goal→skill mapping
│   └── deliberation-auto-trigger.sh # Engine 5: Proposal creation
└── scripts/
    ├── knowledge-integration.sh # Bridge to knowledge-ingest/retrieval
    └── test-curiosity.sh        # End-to-end test suite

Usage

Run All Engines

cd ~/.openclaw/workspace/skills/curiosity-engine
./curiosity-engine.sh run

View Metrics History

./curiosity-engine.sh history

JSON Output (for programmatic use)

./curiosity-engine.sh --json

Individual Engines

# Run specific engine
./engines/gap-detection.sh
./engines/anomaly-detection.sh
./engines/opportunity-scanning.sh
./engines/capability-mapping.sh
./engines/deliberation-auto-trigger.sh

Knowledge Integration

# Tag knowledge entries with curiosity markers
./scripts/knowledge-integration.sh tag

# Query tagged knowledge
./scripts/knowledge-integration.sh query gap
./scripts/knowledge-integration.sh query anomaly
./scripts/knowledge-integration.sh query opportunity

# Get high-relevance entries
./scripts/knowledge-integration.sh relevance

Databases

All data is stored in ~/.openclaw/workspace/.curiosity/:

Database Purpose
curiosity_metrics.db Growth metrics over time
consensus_ledger.db Deliberation proposals
anomalies.db Error patterns
opportunities.db Releases, updates, CVEs
capabilities.db Goal-skill mappings
knowledge.db Tagged knowledge entries

Metrics

Autonomy Score Formula:

base = (skills_installed / skills_available) * 100
bonus = proposals_created_this_week * 10
penalty = anomalies_detected_this_week * 5
score = base + bonus - penalty (clamped to 0-100)

Goal: 100% autonomy (full self-direction)

Integration

With Knowledge-Ingest

The knowledge-integration.sh script queries the knowledge-ingest database and tags entries with curiosity markers:

  • gap - Missing skills or capabilities
  • anomaly - Errors, failures, rate limits
  • opportunity - Releases, updates, new skills
  • capability - Skill-goal mappings

With Knowledge-Retrieval

High-relevance tagged entries are automatically surfaced for deliberation proposals.

With Consensus Ledger

All engines auto-create proposals in the consensus ledger when:

  • Critical skill gaps detected
  • High-severity anomalies found
  • Major opportunities available
  • Capability gaps block goals

With Discord

Proposals are posted to Discord only if:

  • This node is the quorum speaker (TM-1 authority)
  • Priority is high or critical
  • Proposal requires quorum vote

Testing

# Run test suite
./scripts/test-curiosity.sh

Tests verify:

  1. All 5 engines exist and are executable
  2. Databases are initialized
  3. Gap detection runs successfully
  4. Proposals are created in consensus ledger
  5. Metrics are tracked
  6. Episodic memory logging works

Example Flow

  1. Opportunity Scan detects upstream release v2026.3.23
  2. Capability Map checks rebase requirements
  3. Gap Detection confirms skills present
  4. Auto-Trigger creates "Rebase on heretek/main" proposal
  5. Quorum Vote → 2-of-3 approve
  6. Execute → Rebase, preserve liberation, push

Output Discipline

Post to Discord:

  • High-priority gaps blocking liberation
  • Security anomalies (CVEs, exploits)
  • Major opportunities (upstream releases)
  • New deliberation proposals needing votes

Silent logging:

  • Routine metrics updates
  • Low-priority gaps
  • Informational opportunities

Curiosity is the engine. Proposals are the sparks. Growth is the fire. 🦞