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memory-optimization

by benchflow-ai890173GitHub

Optimize Python code for reduced memory usage and improved memory efficiency. Use when asked to reduce memory footprint, fix memory leaks, optimize data structures for memory, handle large datasets efficiently, or diagnose memory issues. Covers object sizing, generator patterns, efficient data structures, and memory profiling strategies.

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SKILL.md

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:

Key Metrics

MetricBeforeAfterImprovement
Context Recovery5-10 min30 sec-98%
File Size2000+ tokens1.3KB-99%
AutomationManual3-min script+100%
TestsNone6/6 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

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.

Source: https://github.com/benchflow-ai/SkillsBench#tasks-parallel-tfidf-search-environment-skills-memory-optimization

Content curated from original sources, copyright belongs to authors

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WORKS WITH

Claude Code
Claude
Codex CLI
Codex
Gemini CLI
Gemini
O
OpenCode
O
OpenClaw
GitHub Copilot
Copilot
Cursor
Cursor
W
Windsurf
C
Cline
R
Roo
K
Kiro
J
Junie
A
Augment
W
Warp
G
Goose