Llm council

Skill rohitg00/pro-workflow/skills/llm-council

Claude Code learns from your corrections: self-correcting memory that compounds over 50+ sessions. Context engineering, parallel worktrees, agent teams, and 17 battle-tested skills.

Install

npx -y skills add rohitg00/pro-workflow --skill llm-council

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What its author says it does

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Provider-agnostic multi-LLM deliberation. Three phases — independent responses, cross-model anonymized ranking, chairman synthesis. Provider config from env (OPENAI/ANTHROPIC/FIREWORKS/OPENROUTER/custom OpenAI-compatible base URL). Persists transcript to a wiki page when --wiki <slug> 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.

SKILL.md

3.7 KB, as published. Nobody here has run it

LLM Council

Karpathy's LLM Council pattern, provider-agnostic. dair-academy's version hardcoded Fireworks; ours reads any OpenAI-compatible endpoint via env.

When to use

High-stakes plan review (/plan crosses 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

Provider chosen via env. First-match wins:

Env var	Provider	Default base URL
`ANTHROPIC_API_KEY`	Anthropic	`https://api.anthropic.com`
`OPENAI_API_KEY`	OpenAI	`https://api.openai.com/v1`
`OPENROUTER_API_KEY`	OpenRouter	`https://openrouter.ai/api/v1`
`FIREWORKS_API_KEY`	Fireworks	`https://api.fireworks.ai/inference/v1`
`LLM_COUNCIL_BASE_URL` + `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

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.