Skill2.3k repo starsupdated 4d ago

llm-council

llm-council executes a three-phase multi-model deliberation pattern where independent LLMs generate responses, rank anonymized peer outputs, and a chairman synthesizes findings. Use it for high-stakes decisions requiring multiple perspectives, architecture reviews, or conflicting rule resolution. Supports provider-agnostic configuration (Anthropic, OpenAI, Fireworks, OpenRouter, custom endpoints) and optionally persists deliberation transcripts to wiki pages for documentation and searchability.

View source Repository: pro-workflow

Install in Claude Code

Copy

git clone --depth 1 https://github.com/rohitg00/pro-workflow /tmp/llm-council && cp -r /tmp/llm-council/skills/llm-council ~/.claude/skills/llm-council

Then start a new Claude Code session; the skill loads automatically.

Definition

SKILL.md

# 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

1. **Independent**: each model answers in parallel
2. **Ranking**: each model ranks anonymized peer responses
3. **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

1. Never skip the ranking phase. It's the core of the council pattern.
2. Save raw responses to disk verbatim. No summarization in storage.
3. Anonymize responses for ranking — models see `Response A/B/C/...`, not peer names.
4. The chairman sees both real names AND rankings.
5. 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`.