Agent skill

context-engineer

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

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/context-engineer

Metadata

Additional technical details for this skill

openclaw 
{
    "emoji": "\ud83d\udd2c",
    "homepage": "https://github.com/cacheforge-ai/cacheforge-skills",
    "requires": {
        "bins": [
            "python3"
        ]
    }
}

SKILL.md

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

bash
# 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

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