How do I install the Context Engineer skill?

Run npx skillmds add majiayu000/context-engineer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Context Engineer safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Context Engineer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Context Engineer free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Context Engineer?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Context Engineer by majiayu000 — Agent Skill

Name: Context Engineer
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

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

---name: context-engineerdescription: Context window optimizer — analyze, audit, and optimize your agent's context utilization. Know exactly where your tokens go before they're sent.license: MIT--- ## 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

Frequently asked questions