skilld
All skills
am-will avatar

/llm-council

@c4bd1f3
by am.willam-will/codex-skills1k stars
60

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.

Ask your Agent

Use this Skill: https://skilld.dev/gh/am-will/codex-skills/llm-council

This session only. Nothing lands on disk.

referencescli-notes.md

≈394 tokens on demand. Your agent reads this file only when SKILL.md points to it.

CLI Notes (Context7)

Codex CLI

  • Non-interactive execution: codex exec (or codex e).
  • JSON streaming: codex exec --json "..." outputs JSON Lines events to stdout.
  • Structured output: codex exec --output-schema ./schema.json -o ./output.json "...".
  • Final message is written to stdout; streaming activity goes to stderr.
  • Model override: codex exec -m gpt-5.2-codex -c model_reasoning_effort=xhigh "...".

Claude Code

  • Launch interactive agent: claude in the repo directory.
  • Non-interactive print mode: claude -p "query" (prints response and exits).
  • JSON output: claude -p "query" --output-format json.
  • Schema-validated JSON: claude -p --json-schema '<schema>' "query" (print mode only).
  • Model selection: claude -p --model opus "query" (alias for latest Opus).
  • Claude CLI accepts a full model name via --model (example in docs uses claude-sonnet-4-5-20250929).
  • Debug mode: claude --debug.

Gemini CLI

  • Non-interactive prompt: gemini -p "...".
  • Structured JSON output: gemini -p "..." --output-format json.
  • Streaming JSON events: gemini -p "..." --output-format stream-json.
  • Model selection: gemini -p "..." --model gemini-3-pro-preview (requires preview features enabled).

OpenCode CLI

  • Non-interactive prompt: opencode run "...".
  • Run flags include --model (provider/model), --agent, --format (default or json), and --attach to a running server.
  • --format json returns raw JSON events; default format prints text.
  • List available models with opencode models (optionally --refresh).

Source: SKILL.md on GitHub

Third-party checks

1 alert16d5 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    This skill provides a robust orchestration framework for multi-agent planning and judging. It includes significant security best practices, such as local-only networking, token-based authentication for its UI, and redaction of sensitive data like API keys.

  • Socket16d

    2 alerts: gptAnomaly, gptSecurity

  • Snyk16d

    Risk: LOW · No issues

  • Runlayer7mo

    9/22 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at c4bd1f3. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 months ago.

Steadyupdated 8 months ago

Topics

  • CLI
  • llm
  • multi-agent
  • planning
  • orchestration
  • codex
  • claude
  • gemini
  • json
  • markdown

README badge

README badge for am-will/codex-skills/llm-council

What it does

Orchestrates a multi-agent planning council (Codex, Claude, Gemini, OpenCode, or custom) that produces independent implementation plans, anonymizes them, and judges them into a single final plan. Use this skill when you need bias-resistant planning with structured JSON outputs, retries, and failure handling across multiple CLI agents running in parallel.

Generated from the current SKILL.md.

Frequently asked

Can I use different LLM providers (Claude, GPT, Gemini) in the same council?
Yes. The skill supports configuring multiple planners with different providers via the task spec — Codex, Claude Code, Gemini, OpenCode, or custom CLI commands. Each planner runs independently in parallel.
How long does the council take to run?
Plans can take significant time to build. The skill enforces a 30-minute session window to allow all phases (planning, anonymization, judging) to complete. You should not close the session until the final-plan.md is saved.
What output format does the council produce?
Plans are produced as Markdown files saved under ./llm-council/runs/<timestamp>, including individual planner outputs, a judge.md, and a final merged final-plan.md for auditability.
Do I need to configure agents before running?
The skill checks for an existing agents.json config file first. If none exists, you must run ./setup.sh to configure which planning agents to use. You can also pass agent config inline via the task spec.
What happens if a planner agent fails?
The skill retries failed agents up to 2 times. If any agent still fails after retries, the council yields and alerts you to fix the issue before the judge phase can proceed.

Generated from the current SKILL.md. These answers refresh after source changes.