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 ```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://anvil-ai.io