| name | llm-council |
| description | Run a question through 5 AI advisors who analyze independently, peer-review anonymously, then synthesize a verdict. Invoke for strategic decisions with genuine uncertainty where multiple perspectives add value. |
LLM Council
Multi-perspective analysis based on Andrej Karpathy's LLM Council methodology. Five independent advisors, anonymous peer review, one synthesized verdict.
When to Invoke
- Strategic decisions where being wrong is expensive
- Trade-offs where reasonable people genuinely disagree
- High-stakes choices where blind spots could be costly
Not for: factual lookups, creation tasks, or questions with one right answer.
The Advisors
| Advisor | Orientation |
|---|
| Contrarian | Finds fatal flaws and hidden risks |
| First Principles | Strips to fundamentals, questions assumptions |
| Expansionist | Finds upside everyone else is missing |
| Outsider | Zero domain context — fresh eyes |
| Executor | What do you actually do Monday morning? |
Full persona descriptions and prompt templates: Council Playbook
Process
Step 1 — Frame the Question
Take the user's raw question and reframe it as a clear, neutral prompt:
- State the core decision or question
- Add key context and constraints from the user
- Identify what's at stake — why this decision matters
If the question is too vague to council, ask one clarifying question. Then proceed.
Step 2 — Independent Analysis (5 parallel sub-agents)
Spawn all 5 advisors simultaneously using the Agent tool. Recommended model for advisors: sonnet-4.5 (cost/quality balance — each persona is narrow and short-form; Opus×5 in parallel is ~5× cost for marginal gain). Each advisor receives:
- Their persona from the Council Playbook
- The framed question
- Instruction: respond in 150-300 words, don't hedge, lean fully into your assigned angle
All 5 must run in parallel. Sequential spawning lets earlier responses bleed into later ones.
Step 3 — Peer Review (5 parallel sub-agents)
Collect all 5 responses. Anonymize them as Response A through E — randomize the mapping so there's no positional bias.
Spawn 5 new sub-agents in parallel. Recommended model for reviewers: sonnet-4.5 (structural comparison across 5 short texts — well within Sonnet's range). Each reviewer sees all 5 anonymized responses and answers:
- Which response is the strongest and why? (pick one)
- Which response has the biggest blind spot?
- What did ALL responses miss?
Each review: under 200 words.
Step 4 — Chairman Synthesis + Output
Spawn one Chairman sub-agent. Recommended model: opus-4.7 with extended thinking (high effort) — 11-input synthesis is the cognitive bottleneck of this skill. The Chairman receives everything de-anonymized: original question, all 5 advisor responses, all 5 peer reviews.
Reasoning approach: Before writing the verdict, reason silently through:
- Which points appear in ≥3 advisor responses (convergence signal)?
- Where do reviewers disagree about the strongest response (genuine clash)?
- Which blind spots emerged ONLY in peer review, not in original advisors?
Identify at least 2 non-obvious findings before structuring output. Do not stream a first draft — produce a single considered verdict.
Produces the verdict in this structure:
- Where the Council Agrees — convergence points (high-confidence signals)
- Where the Council Clashes — genuine disagreements with both sides
- Blind Spots the Council Caught — emerged only through peer review
- The Recommendation — a clear answer, not "it depends"
- The One Thing to Do First — one concrete next step
The chairman can disagree with the majority if the dissenter's reasoning is strongest.
Display the full verdict directly to the user.
Output
- Verdict: displayed in conversation immediately after synthesis
- Transcript: saved to
./plans/council-{topic}-{YYYY-MM-DD}.md with: original question, framed question, all 5 advisor responses, all 5 peer reviews (with anonymization mapping revealed), and the chairman's full synthesis
Acceptance Checklist
