context-engineer

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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: - cacheforge - context-engineering - token-optimization - llm - ai-agents - prompt-optimization - observability - discord - discord-v2 metadata: {"openclaw":{"emoji":"🔬","homepage":"https://github.com/cacheforge-ai/cacheforge-skills","requires":{"bins":["python3"]}}}

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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.json

What 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

--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)

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