LLM Council Agent Skill

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.