- name
- context-engineer
- version
- 1.0.0
- description
- Context window optimizer — analyze, audit, and optimize your agent's context utilization. Know exactly where your tokens go before they're sent.
- author
- Anvil AI
- license
- MIT
- homepage
- https://github.com/cacheforge-ai/cacheforge-skills
- user-invocable
- true
- tags
- metadata
- {"openclaw":{"emoji":"🔬","homepage":"https://github.com/cacheforge-ai/cacheforge-skills","requires":{"bins":["python3"]}}}
When to use this skill
Use this skill when the user wants to:
- Understand where their context window tokens are going
- Analyze workspace files (SKILL.md, SOUL.md, MEMORY.md, etc.) for bloat
- Audit tool definitions for redundancy and overhead
- Get a comprehensive context efficiency report
- Compare before/after snapshots to measure optimization progress
- Optimize system prompts for token efficiency
Commands
# Analyze workspace context files — token counts, efficiency scores, recommendations
python3 skills/context-engineer/context.py analyze --workspace ~/.openclaw/workspace
# Analyze with a custom budget and save a snapshot for later comparison
python3 skills/context-engineer/context.py analyze --workspace ~/.openclaw/workspace --budget 128000 --snapshot before.json
# Audit tool definitions for overhead and overlap
python3 skills/context-engineer/context.py audit-tools --config ~/.openclaw/openclaw.json
# Generate a comprehensive context engineering report
python3 skills/context-engineer/context.py report --workspace ~/.openclaw/workspace --format terminal
# Compare two snapshots to see projected token savings
python3 skills/context-engineer/context.py compare --before before.json --after after.jsonWhat It Analyzes
- System prompt efficiency — Length, redundancy detection, compression potential
- Tool definition overhead — Count tools, per-tool token cost, identify unused/overlapping
- Memory file bloat — MEMORY.md size, stale entries, optimization suggestions
- Skill overhead — Installed skills contributing to context, per-skill token cost
- Context budget — What % of model context window is consumed by static content vs available for conversation
Options
--workspace PATH— Path to workspace directory (default:~/.openclaw/workspace)--config PATH— Path to OpenClaw config file (default:~/.openclaw/openclaw.json)--budget N— Context window token budget (default: 200000)--snapshot FILE— Save analysis snapshot to FILE for later comparison--format terminal— Output format (currently: terminal)
Notes
- Token estimates are approximate (~4 characters per token). For precise counts, use a model-specific tokenizer.
- No external dependencies required — runs with Python 3 stdlib only.
- Built by Anvil AI — context engineering experts. https://anvil-ai.io