domains / families / agent-tooling / llm-council

llm-council

combined

Orchestrate a configurable, multi-member CLI planning council (Codex, Claude Code, Gemini, OpenCode, or custom) to produce independent implementation plans, anonymize and randomize them, then judge and merge into one final plan. Use when you need a robust, bias-resistant planning workflow, structured JSON outputs, retries, and failure handling across multiple CLI agents. Run Fireworks-hosted open-weight model councils that compare responses and synthesize a final answer.

When to use

• **Phase 1**: All models respond to the query independently (parallel)
• **Phase 2**: Models rank each other's anonymized responses
• **Phase 3**: A Chairman LLM synthesizes the final answer

When not to use

• Requires the upstream tool, account, API key, or local setup when the workflow names one.
• Does not authorize destructive, production, paid, or external-message actions without explicit user approval.
• Validate generated artifacts or recommendations against the user's real sources before treating them as final.

Skill page

Conflict

The two copies describe different implementations of the same 'LLM Council' concept. The first (am-will) is a local CLI orchestrator that runs multiple coding agents (Codex, Claude, OpenCode) in parallel, anonymizes their plans, and uses a judge to merge them into a final implementation plan. The second (sickn33) is a Fireworks AI-based workflow that uses open-weight models to answer a query, rank anonymized responses, and synthesize a final answer. They differ in infrastructure (local CLI vs. Fireworks API), agent types (coding agents vs. open-weight LLMs), and output (implementation plan vs. synthesized answer).