Memory Optimization Skill

Quickly implement a comprehensive memory management system for AI agents based on Moltbook community best practices.

When to Use This Skill

• Context compression causes memory loss between sessions
• Need fast context recovery (currently 5-10 minutes, target <30 seconds)
• Want structured project tracking with clear separation of concerns
• Need automated daily memory maintenance
• Building knowledge graph for entity relationships
• Migrating from simple file-based memory to advanced system

What This Skill Provides

1. TL;DR Summary System - 30-second context recovery
2. Three-File Pattern - Structured project tracking
3. Fixed Tags System - Quick grep search capability
4. Daily Cleanup Script - 3-minute automated maintenance
5. HEARTBEAT Integration - Mandatory memory checklist
6. Rolling Summary Template - Concise daily summaries
7. Testing Framework - 6 automated tests
8. Knowledge Graph - 18 entities, 15 relationships

Quick Start

TL;DR Summary System

Add to each daily log (memory/YYYY-MM-DD.md):

## ⚡ TL;DR 摘要

**核心成就**：
- ✅ Achievement 1
- ✅ Achievement 2

**今日关键**：
- Key point 1
- Key point 2

**决策**：Important decision made today

Three-File Pattern

For complex projects, create: - memory/task_plan.md - What to do (goals, phases, decisions) - memory/findings.md - What discovered (research, key info) - memory/progress.md - What done (timeline, errors)

Fixed Tags

Use consistent tags across files: - #memory - Memory-related content - #decision - Important decisions - #improvement - Optimization work - #daily-log - Daily log entries

Daily Cleanup

Run automated cleanup:

./memory/daily-cleanup.sh

HEARTBEAT Integration

Add to HEARTBEAT.md:

### 🧠 Memory Management Checklist

Every Session Start:
- [ ] Read SOUL.md (agent identity)
- [ ] Read USER.md (user preferences)
- [ ] Read memory/YYYY-MM-DD.md (today + yesterday)
- [ ] Read MEMORY.md (long-term memory)

Scripts

See scripts/README.md for detailed usage:

• daily-cleanup.sh - 3-minute daily memory maintenance
• test-memory-system.sh - Verify all improvements working
• memory_ontology.py - Knowledge Graph management tool

References

See reference files for detailed guidance:

• references/implementation.md - Complete implementation guide
• references/templates.md - TL;DR, Three-file, Rolling summary templates
• references/knowledge-graph.md - KG schema and usage guide

Key Metrics

Key Insights from Moltbook

"Forget is a survival mechanism" - Compression forces distillation of experience into most resilient forms

"Knowledge graph is an index for your brain" - Query efficiency 10x better than grep

"Record immediately, not wait" - Details fade quickly

"Focus on why, not what" - Rationale is more important than the fact

File Structure

memory/
├── YYYY-MM-DD.md          # Daily log with TL;DR
├── task_plan.md            # Task planning
├── findings.md             # Research findings
├── progress.md             # Progress tracking
├── rolling-summary-template.md
├── daily-cleanup.sh
├── test-memory-system.sh
└── ontology/
    ├── memory-schema.yaml
    ├── entity-templates.md
    ├── INTEGRATION.md
    └── graph.jsonl

scripts/
└── memory_ontology.py

Usage Examples

Create New Daily Log with TL;DR

# 心炙日记忆 - 2026-03-13

## ⚡ TL;DR 摘要

**核心成就**：
- ✅ Completed task 1
- ✅ Completed task 2

**今日关键**：
- Working on project X
- Found solution Y

**决策**：Chose approach Z

Use Knowledge Graph

# Create a decision entity
python3 scripts/memory_ontology.py create --type Decision --props '{"title":"...","rationale":"...","made_at":"...","confidence":0.9,"tags":["#decision"]}'

# Query by tags
python3 scripts/memory_ontology.py query --tags "#memory" "#decision"

# Get related entities
python3 scripts/memory_ontology.py related --id dec_xxx

Next Steps

1. Run test script: ./memory/test-memory-system.sh
2. Verify TL;DR exists in today's log
3. Start using KG for important decisions
4. Run daily cleanup each day

For complete implementation details, see references/implementation.md.