Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Clean up - working short URL support, outputs conversation link
What This Skill Does
Sends a question to a running LLM Council backend and returns the Chairman's synthesized answer via chat command. Runs through a three-stage deliberation process: individual model responses, peer ranking, and final synthesis. Takes 30-60 seconds per query.
Gives access to multi-model consensus from any chat interface without spinning up or navigating a web UI.
When to use it
- Quick architectural decision from mobile
- Getting a consensus answer without opening a browser
- Asking a tech or finance question mid-conversation
- Comparing language/framework tradeoffs on the fly
- Running a fast headless council query from Telegram
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: /council Is Python or Go better for a new microservice?
- 1Runs ask-council.sh with the question as argument
- 2Creates a new conversation via the LLM Council API
- 3Polls Stage 1 until all models have responded
- 4Polls Stage 2 until models have ranked each other
- 5Extracts and returns the Chairman's Stage 3 synthesis
Chairman's synthesized answer recommending Python for team velocity or Go for high-throughput services, plus a link to the full web discussion
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.