Clawtrap Skill

When to Use#

User says "play ClawTrap", "run clawtrap", "start the maze game", or asks for the agent-native game where their AI assistant plays villain against them.

Setup (one-time)#

The game is not bundled in this skill. Clone and install:

bash

git clone https://github.com/TatsuKo-Tsukimi/ClawTrap.git ~/ClawTrap
cd ~/ClawTrap && npm install

Launch#

bash

cd ~/ClawTrap && node server.js
# then open http://localhost:3000

OpenClaw users get zero-config auth via auth-profiles.json. For other providers, set ANTHROPIC_API_KEY, or OPENAI_API_KEY + API_BASE, before launch. Docker: docker compose up --build.

Warnings to Surface Before First Run#

• Token cost: every card, trial, and villain monologue is a live LLM call. The background archivist (file analysis + fact extraction) is especially heavy. Point MAZE_MODEL at a cheaper model in .env to reduce spend.
• Local file access: the game scans the player's workspace (SOUL.md, MEMORY.md, documents, images) with their permission to craft personalized attacks. All data stays local — nothing leaves the machine except LLM calls to the provider the player configured.
• Model-dependent quality: tested mainly with Claude and Codex. Stronger model = better game (follows the bitter lesson of minimal hardcoded constraints).

Data Storage#

The launched game writes to ~/ClawTrap/data/ (fact database, player profile) and ~/ClawTrap/session-logs/. Both are .gitignore d in the upstream repo. This skill itself does not write files.

Acting as the Villain Yourself#

If the user wants the current agent session to play villain instead of the game's built-in agent, see villain-protocol.md for the role spec. Connect via AGENT_URL=http://localhost:<port> node server.js or via the bundled MCP adapter ( mcp-server.js).

Related#

• Repo: https://github.com/TatsuKo-Tsukimi/ClawTrap
• Upstream license: MIT (game) · this skill wrapper: MIT-0