OpenClaw Dual Agent

Run two OpenClaw agents simultaneously — a paid Anthropic agent and a free agent using either OpenRouter or local Ollama models. Trigger phrases: multi-agent...

This skill is mostly coherent for its stated purpose, but there are a few things to check before installing: - Expect to provide credentials: you will need your Anthropic API key for the paid agent, and (only for the cloud free-agent option) an OpenRouter API key placed in auth-profiles.json in the agentDir. You will also need Telegram bot tokens for each bot. The registry metadata did not declare these, so don't be surprised when the setup asks for them. - Keep secrets safe: follow the SKILL.md advice — use interactive onboarding where possible, set auth files to chmod 600, and avoid passing API keys on the CLI or storing them in world-readable files. Consider using a secrets manager instead of plaintext files. - Verify endpoints and packages: the instructions reference https://openrouter.ai and https://ollama.ai (and Homebrew). Confirm these providers are expected by you and that model IDs referenced are legitimate. - Local option is privacy-friendly: choosing the Ollama/local route avoids sending data to cloud providers after models are pulled, but it requires large downloads and running 'ollama serve'. - Packaging mismatch: the registry metadata missing required env vars is a packaging issue (sloppy but not necessarily malicious). If you need higher assurance, request the publisher clarify the required environment variables and confirm exactly what files the skill will read/write under ~/.openclaw. If you plan to proceed, review and back up any existing ~/.openclaw configuration, create per-agent auth files with restrictive permissions, and test the setup in a controlled environment before connecting production accounts.

Type: OpenClaw Skill Name: openclaw-dual-agent Version: 1.1.1 The bundle provides comprehensive documentation and configuration templates for running dual OpenClaw agents using Anthropic and OpenRouter or local Ollama models. It emphasizes security best practices, such as using interactive onboarding to prevent API keys from appearing in shell history and providing commands to mask secrets in logs. No malicious execution patterns, data exfiltration, or suspicious instructions were found in SKILL.md or the reference files.