Skill: richardiitse/memory-optimization (version 1.0.4)

--- name: memory-optimization version: 1.0.4 license: MIT description: | Comprehensive memory management optimization for AI agents. Use when: (1) Agent experiences context compression amnesia, (2) Need to rebuild context quickly after session restart, (3) Want structured memory system with TL;DR summaries, (4) Need automated daily memory maintenance, (5) Want to implement knowledge graph for entity management, or (6) Building agent memory system from scratch. Provides: TL;DR summary system, Three-file pattern (task_plan/findings/progress), Fixed tags system, Daily cleanup automation, HEARTBEAT integration, Rolling summary template, Testing framework, and Knowledge Graph integration. --- # 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 9. **Skill Usage Tracker** - Track and analyze skill usage patterns ## Quick Start ### TL;DR Summary System Add to each daily log (memory/YYYY-MM-DD.md): ```markdown ## ⚡ TL;DR Summary **Core Achievements**: - ✅ Achievement 1 - ✅ Achievement 2 **Today's Key Points**: - Key point 1 - Key point 2 **Decisions**: 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: ```bash ./memory/daily-cleanup.sh ``` ### HEARTBEAT Integration Add to HEARTBEAT.md: ```markdown ### 🧠 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](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 (with Phase 6 retrieve command) - `kg_type_fixer.py` - Fix entities missing type field by inferring from ID prefix - `kg_extractor.py` - KG extraction from agent sessions (LLM-driven) - `write_time_gating.py` - Phase 8: Write-time gating evaluation - `archived_memory_store.py` - Cold storage management (Phase 8) - `preference_engine.py` - Phase 2: User preference inference from conversation history - `consolidation_engine.py` - Phase 3: consolidate similar episodes into SkillCards - `decay_engine.py` - Batch decay engine for memory strength management - `entity_dedup.py` - Phase 2: Embedding-based entity deduplication and merging - `working_memory.py` - Phase 5: Context Window layered compression (3 levels) - `memory_loader.py` - Phase 6: Proactive memory recovery (3-stage staged loading) - `memory_dashboard.py` - Phase 7: Memory health dashboard (Health Score, decay forecast) ## Phase 6: Value-Aware Retrieval KG entities can be retrieved by value score (weighted combination of reliability, strength, significance, preference match, recency): ```bash # Retrieve entities sorted by value score python3 scripts/memory_ontology.py retrieve --types Decision Finding --min-score 0.4 # Query by text and filter by score python3 scripts/memory_ontology.py retrieve --query "architecture" --show-scores # CLI help python3 scripts/memory_ontology.py retrieve --help ``` ## References See reference files for detailed guidance: - [references/implementation.md](references/implementation.md) - Complete implementation guide - [references/templates.md](references/templates.md) - TL;DR, Three-file, Rolling summary templates - [references/knowledge-graph.md](references/knowledge-graph.md) - KG schema and usage guide ## Key Metrics | Metric | Before | After | Improvement | |--------|--------|-------|-------------| | Context Recovery | 5-10 min | 30 sec | -98% | | File Size | 2000+ tokens | 1.3KB | -99% | | Automation | Manual | 3-min script | +100% | | Tests | None | 6/6 pass | +100% | ## 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 ``` ### Skill Usage Tracker Track and analyze skill usage patterns via Knowledge Graph: ```bash # Record a skill usage python3 scripts/skill_tracker.py record --skill coding-agent --status success --duration 2.5 # View usage statistics python3 scripts/skill_tracker.py stats # Scan session history and save to KG python3 scripts/skill_tracker.py scan --save ``` **Features:** - Record skill invocations with status, duration, and error info - Automatic skill categorization (feishu/apple/coding/memory/api/system/utility) - Session history scanning for automatic usage detection - KG-backed storage with fallback to file ## Usage Examples ### Create New Daily Log with TL;DR ```markdown # Daily Memory - 2026-03-13 ## ⚡ TL;DR Summary **Core Achievements**: - ✅ Completed task 1 - ✅ Completed task 2 **Today's Key Points**: - Working on project X - Found solution Y **Decisions**: Chose approach Z ``` ### Use Knowledge Graph ```bash # 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 ``` ## Environment Variables ```bash # GLM API configuration (used by kg_extractor.py) export OPENAI_API_KEY="your-glm-token" export OPENAI_BASE_URL="https://open.bigmodel.cn/api/paas/v4" export OPENAI_MODEL="glm-5" # Global KG path (optional, defaults to ~/.openclaw/workspace/memory/ontology) # Configure via KG_DIR environment variable ``` ## KG Sharing Across Agents Multi-agent setups can share a single Knowledge Graph for collaborative memory. ### Setup ```bash # 1. Create shared directory mkdir -p ~/.openclaw/shared-kg # 2. Create symlink to main KG ln -sf ~/.openclaw/workspace/memory/ontology/graph.jsonl ~/.openclaw/shared-kg/main-kg.jsonl ``` ### Usage by Agents Each agent should reference the shared KG in their `TOOLS.md`: ```markdown ## Knowledge Graph (KG) - **共享 KG**: ~/.openclaw/shared-kg/main-kg.jsonl - 包含所有重要的长期记忆、决策、经验教训 ``` ### Script Usage with Shared KG ```bash # Direct KG path KG_DIR=~/.openclaw/shared-kg python3 scripts/memory_ontology.py query --tags "#decision" # Or set in .env KG_DIR=~/.openclaw/shared-kg/ ``` **Benefits:** - All agents access the same entity pool - Decisions and lessons are shared across agents - No duplicate entity creation ## OpenClaw Skill Invocation When the user types `/xmo`, OpenClaw automatically invokes this memory-optimization skill. Add the following to OpenClaw's `settings.json` or `skills.json`: ```json { "skills": { "xmo": { "path": "./memory-optimization", "description": "Memory optimization skill for AI agents" } } } ``` After configuration, the user can activate this skill by typing `/xmo`. ## 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](references/implementation.md).


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)