Agent skill · software engineering · aiskillstore
llm-council
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.
Why this skill is useful
Adds a complex orchestration script for multi-agent planning that includes error handling, session management, and structured output generation.
What it needs
Requires python3 installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
LLM Council Skill Quick start Always check for an existing agents config file first ($XDGCONFIGHOME/llm-council/agents.json or ~/.config/llm-council/agents.json). If none exists, tell the user to run ./setup.sh to configure or update agents. The orchestrator must always ask thorough intake questions first, then generates prompts so planners do not ask questions. Even if the initial prompt is strong, ask at least a few clarifying questions about ambiguities, constraints, and success criteria. Tell the user that answering intake questions is optional, but more detail improves the quality of the final plan. Use python3 scripts/llmcouncil.py run --spec /path/to/spec.json to run the council. Plans are produced as Markdown files for auditability. Run artifacts are saved under ./llm-council/runs/<timestamp> relative to the current working directory. Configure defaults interactively with python3 scripts/llmcouncil.py configure (writes $XDGCONFIGHOME/llm-council/agents.json or ~/.config/llm-council/agents.json). Workflow 1. Load the task spec, and explore the codebase you are in to get a strong sense of the product. 2. Always ask thorough intake questions to build a clear task brief. Clarify any ambiguities, constraints, and success criteria. Remind the user that answers are optional but improve plan quality. 3. Build planner prompts (Markdown template) and launch the configured planner agents in parallel background shells. 4. Collect outputs, validate Markdown structure, and retry up to 2 times on failure. If any agents fails, yield and alert the user to fix the issue. 5. Anonymize plan contents and randomize order before judging. 6. Run the judge with the rubric and Markdown template, then save judge.md and final-plan.md. 7. Session management: DO NOT yield/finish the response until a full 30-minute timer has completed and judge + final-plan.md are confirmed saved; keep the session open during that interval to avoid closing the interface. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/llm-council