Context engineer

Skill cacheforge-ai/cacheforge-skills/skills/context-engineer

⚡ SOTA agent skills for OpenClaw — observability, security, code quality, incident response, and more. Built by Anvil AI.

Install

npx -y skills add cacheforge-ai/cacheforge-skills --skill context-engineer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

10 stars10 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Context window optimizer — analyze, audit, and optimize your agent's context utilization. Know exactly where your tokens go before they're sent.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

3.0 KB, as published. Nobody here has run it

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://labs.anvil-ai.io