---

name: memory-consolidation description: Periodic "sleep-like" memory optimization. Detects patterns, resolves conflicts, applies decay, and transfers knowledge across contexts. metadata: {"clawdbot":{"requires":{"bins":["python3"]}}} user-invocable: true

Memory Consolidation

Cross-session learning through periodic memory optimization, inspired by how biological sleep consolidates memories.

When to Run

Frequency	Type	Duration	Purpose
Daily	Light	~2 min	Health check, quick cleanup
Weekly	Standard	~10 min	Full consolidation
Monthly	Deep	~30 min	System-wide optimization

Consolidation Phases

Phase 1: Pattern Detection

Identify recurring patterns across memories:

• Similar problems solved multiple ways
• Frequently co-accessed memories
• Successful strategies repeated

Phase 2: Schema Formation

Create higher-level abstractions:

• "When X happens, Y usually works"
• Merge similar memories into schemas
• Reduce redundancy

Phase 3: Conflict Resolution

Detect and resolve contradictions:

• Same problem, different solutions stored
• Outdated information conflicting with new
• Decide which to keep based on recency + success

Phase 4: Temporal Decay

Apply time-based forgetting:

• Unused memories decay in Q-value
• Very old, never-accessed memories pruned
• Keep core knowledge stable

Phase 5: Cross-Context Transfer

Share useful patterns between contexts:

• Debugging insight useful for coding
• Research finding applicable to implementation
• Generalize specific solutions

Phase 6: QAVR Sync

Update Q-value rankings based on consolidation:

• Boost patterns with high success
• Demote patterns that didn't work
• Rebalance context weights

Output Report

## Memory Consolidation Report

**Date**: [timestamp]
**Type**: [Light/Standard/Deep]
**Duration**: [X minutes]

### Health Metrics
- Total memories: X
- Active (accessed 30d): X
- Dormant (no access 30d): X
- Conflicts detected: X
- Patterns identified: X

### Actions Taken
- Memories merged: X
- Memories pruned: X
- Schemas created: X
- Conflicts resolved: X
- Cross-context transfers: X

### Top Patterns Identified
1. [pattern description]
2. [pattern description]
3. [pattern description]

### Recommendations
- [suggestion for improvement]

### System Health Score
[X/100]

Usage

Manual Invocation

/memory-consolidation daily   # Quick health check
/memory-consolidation weekly  # Standard consolidation
/memory-consolidation monthly # Deep optimization

Automated Scheduling

Configure in clawdbot.json:

{
  "memory": {
    "consolidation": {
      "daily": "03:00",
      "weekly": "sunday 04:00",
      "monthly": "1st 05:00"
    }
  }
}

Benefits

1. Reduced Noise: Old, unhelpful memories pruned
2. Better Retrieval: Patterns surface useful info
3. Conflict-Free: No contradictory advice
4. Cross-Pollination: Insights shared across domains
5. Efficiency: Less storage, faster queries