context-engineer

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

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Install skill "context-engineer" with this command: npx skills add tkuehnl/context-engineer

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

Source Transparency

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