llm-council
Provider-agnostic multi-LLM deliberation. Three phases — independent responses, cross-model anonymized ranking, chairman synthesis. Credentials come from explicit plugin configuration or pro-workflow-specific CLI variables. Persists transcript to a wiki page when --wiki is passed. Use when the user wants multiple AI perspectives, consensus-building, or the "LLM Council" approach for high-stakes reviews, plan critique, or contested learning rules.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/rohitg00/pro-workflow --skill llm-council- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads llm-council automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
LLM Council
Karpathy's LLM Council pattern with Anthropic and OpenAI-compatible providers.
When to use
- High-stakes plan review (
/plancrosses N-file threshold) - Conflicting learning-rules → re-resolve via vote
- User invokes
/council "<query>"or/wiki council - Architecture decisions where you want multiple viewpoints captured
- Persisting deliberation as a wiki page for future reference
Three phases
- Independent: each model answers in parallel
- Ranking: each model ranks anonymized peer responses
- Synthesis: chairman model reads all responses + rankings → final answer
Provider config
Configure optional keys in the plugin configuration dialog. Use the providers MCP server's run_provider_task tool with task: "council" and args: ["run", "<query>", "--provider", "openai"]. For provider status, pass args: ["providers"]. Never request keys in chat or read them from existing credentials or shell configuration.
For standalone CLI use, explicitly set a pro-workflow-specific variable below. The first configured provider is the default; --provider selects one explicitly. See provider configuration.
| Standalone CLI variable | Provider | Default base URL |
|---|---|---|
PRO_WORKFLOW_ANTHROPIC_API_KEY | Anthropic | https://api.anthropic.com |
PRO_WORKFLOW_OPENAI_API_KEY | OpenAI | https://api.openai.com/v1 |
PRO_WORKFLOW_OPENROUTER_API_KEY | OpenRouter | https://openrouter.ai/api/v1 |
PRO_WORKFLOW_FIREWORKS_API_KEY | Fireworks | https://api.fireworks.ai/inference/v1 |
LLM_COUNCIL_BASE_URL + PRO_WORKFLOW_LLM_COUNCIL_API_KEY | Custom OpenAI-compat | (user-supplied) |
Override per-run with --provider openai|anthropic|openrouter|fireworks|custom.
Default model rosters per provider live in scripts/council.js and can be overridden via --models CSV and --chairman <id>.
Commands
In a plugin session, pass these runner arguments through run_provider_task. The direct commands below are for standalone CLI installations with explicit credentials.
node $SKILL_ROOT/scripts/council.js run "<query>" [--models id1,id2,id3] [--chairman id] [--provider <name>] [--wiki <slug>]
node $SKILL_ROOT/scripts/council.js providers
node $SKILL_ROOT/scripts/council.js show <session-id>
--wiki <slug> writes the full transcript to <wiki>/derived/council/<session-id>.md and registers it via wiki-cli.js page so it shows in FTS5 search.
Output
Each session writes:
~/.pro-workflow/council/<session-id>/
├── config.json # query, models, chairman, provider
├── phase1_responses.json # raw API responses per model
├── phase2_rankings.json # anonymized ranking outputs
├── phase3_synthesis.txt # chairman's final answer
└── final_output.md # human-readable bundle
Console prints the markdown bundle. Pipe to pbcopy / tee as needed.
Hard rules
- Never skip the ranking phase. It's the core of the council pattern.
- Save raw responses to disk verbatim. No summarization in storage.
- Anonymize responses for ranking — models see
Response A/B/C/..., not peer names. - The chairman sees both real names AND rankings.
- Display all three phases to the user. No phase elision.
Cost awareness
The script logs per-call latency + tokens on supported providers. Multiply by your provider rate to estimate. Council cost grows linearly with len(models)^2 (each model ranks all others) plus the chairman.
Default council size: 3-5 models. More models = exponentially more ranking calls.
Use with wiki
/wiki council agent-memory "should we adopt episodic memory in our agents?"
Loads agent-memory wiki context as system prompt prefix, runs council, persists transcript as wiki/derived/council/<id>.md. The transcript becomes searchable via /wiki ask.
FAQ
What does the llm-council skill do?
Provider-agnostic multi-LLM deliberation. Three phases — independent responses, cross-model anonymized ranking, chairman synthesis. Credentials come from explicit plugin configuration or pro-workflow-specific CLI variables. Persists transcript to a wiki page when --wiki is passed. Use when the user wants multiple AI perspectives, consensus-building, or the "LLM Council" approach for high-stakes reviews, plan critique, or contested learning rules.
How do I install llm-council?
Run npx skills add https://github.com/rohitg00/pro-workflow --skill llm-council in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/llm-council/ (personal) or .claude/skills/llm-council/ (project).
Which AI agents can use llm-council?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is llm-council safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains llm-council?
It is published by rohitg00 in rohitg00/pro-workflow. It has 155 recorded installs.
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