llm-councillisted

# LLM Council Skill ## Quick start - Always check for an existing agents config file first (`$XDG_CONFIG_HOME/llm-council/agents.json` or `~/.config/llm-council/agents.json`). If none exists, tell the user to run `./setup.sh` to configure or update agents. - The orchestrator must always ask thorough intake questions first, then generates prompts so planners do **not** ask questions. - Even if the initial prompt is strong, ask at least a few clarifying questions about ambiguities, constraints, and success criteria. - Tell the user that answering intake questions is optional, but more detail improves the quality of the final plan. - Use `python3 scripts/llm_council.py run --spec /path/to/spec.json` to run the council. - Plans are produced as Markdown files for auditability. - Run artifacts are saved under `./llm-council/runs/<timestamp>` relative to the current working directory. - Configure defaults interactively with `python3 scripts/llm_council.py configure` (writes `$XDG_CONFIG_HOME/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. Co