context-engineer

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

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