llm-council-members

name	llm-council-members
description	Selects and role-designs LLM council members - provider-diverse 3-5 model panels, cognitive lenses over personas, role-to-model routing, anonymization and identity effects, persona-drift countermeasures. Use for "which models should sit on the council", "pick council members", "design advisor personas/lenses". Not for pipeline topology, aggregation math, or stage prompt wording.

Purpose

Decide who sits on an LLM council and what role each seat plays. The single failure mode this skill exists to prevent: mistaking persona count for model diversity — five prompt variations on one model producing theatrical diversity (different voices) instead of epistemic diversity (different blind spots that can actually catch each other's errors). Deeper evidence, full citations, and persona-engineering detail live in references/composition.md.

When to use / when NOT to use

Use this skill when the user is choosing which models to seat on a council or what role/lens each seat should carry — including when they describe the problem without saying "council" (e.g. "is GPT+Claude+Gemini actually more diverse than three GPT personas?", "does it matter if my reviewers are secretly the same model in different hats?").

Not for, use the sibling instead:

• Pipeline topology, stage count, stopping rules → llm-council-architecture
• Whether to run a council at all vs. one strong model → llm-council-when
• Exact wording of advisor/peer-review/chairman prompts → llm-council-prompts
• Combining/scoring member outputs numerically → llm-council-aggregation
• Token/call budget tradeoffs → llm-council-cost
• Generic "which LLM is best/cheapest for X" with no council in view → not in scope for any skill here; answer directly
• Fine-tuning a model → not in scope for any skill here
• Hiring human team members / advisory board → not in scope for any skill here

Core rule

Diversify provider/training lineage, not persona count. Same-model personas are prompt variations over one fixed set of weights: they hallucinate the same facts and miss the same errors together. Across 350+ models, when two models both err they pick the same wrong answer ~60% of the time, and this error correlation is higher among more-accurate models — improving models converge rather than diverge ("Correlated Errors in Large Language Models," ICML 2025, arxiv.org/abs/2506.07962). A 9-judge panel across 7 model families carried only ~2 independent votes of information (arxiv.org/abs/2605.29800). Homogeneous debate can underperform isolated self-correction via sycophantic conformity — modal adoption of one member's initial answer up to 85.5% ("The Cost of Consensus," arxiv.org/abs/2605.00914).

Heterogeneity is necessary but not sufficient — pair it with structure: dynamic role-to-model assignment beat uniform assignment by up to 74.8% and random by up to 29.7% (arxiv.org/abs/2601.17152, author-reported); adaptive routing plus early stopping atop heterogeneous debate beats fixed-round debate on accuracy and token cost (HCP-MAD, arxiv.org/abs/2604.09679). Provider identity correlated with behavior even in cooperation games (arxiv.org/abs/2605.29874 — single-author, directional).

Workflow

1. Establish provider diversity first. Minimum one model each from different providers — e.g. Anthropic + OpenAI + Google as a baseline triad, adding xAI or a strong open-weight model for more spread on higher-stakes/ambiguous tasks. Two tiers of one vendor's family (flagship + mini) adds negligible diversity — they share training data and RLHF process; prefer one model each from two vendors over two tiers from one.
2. Size the panel at 3-5 members for most cases. Azure's own multi-agent guidance caps group-chat-style orchestration at about three or fewer agents to stay controllable (as of mid-2026 — verify against learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns before treating as current). More members past 5 rarely add independent information (see the 9-judge/2-vote finding above).
3. Check whether heterogeneity is even worth the tax for this task. Counterpoint to encode honestly: Self-MoA — repeatedly sampling a single strong model — can beat mixing in weaker heterogeneous models, +6.6pp on AlpacaEval (arxiv.org/abs/2502.00674); a weak proposer dilutes a mixed panel. Heterogeneity earns its keep on ambiguous/value-laden tasks with no single ground truth. On simple factual tasks, don't pay for it — recommend a single strong model instead and say so plainly.
4. Design lenses as cognitive constraints, not costumes. Mandate a behavior ("required to find at least one flaw") rather than assign an identity ("you are a skeptic") — identity framings invite roleplay, behavioral mandates force actual divergence. Offer the standard 5-lens set (Contrarian, First Principles, Expansionist, Outsider, Executor); leave exact prompt text to llm-council-prompts.
5. Route roles deliberately, and round-robin lenses across members run to run so blind spots decorrelate on both the model axis and the lens axis — don't let one model always own the same lens. Assign the strongest available model to synthesis/chairman duty rather than making it a standing generator — synthesis is a different skill from generation (practitioner consensus, directional).
6. Anonymize members to each other and to the judge/chairman. Revealing real model identities during deliberation increased behavioral convergence (cosine similarity 0.56→0.77, p=0.001, arxiv.org/abs/2604.00026) — identity signals erode the diversity the panel exists to capture. Keep the anonymization scheme intact through full deliberation, not just peer-review (mechanics: llm-council-prompts).
7. If persona/role prompts are used, recommend structured persona fields (role, expertise, perspective, tone, assertiveness, avoid-list) over free text, under ~150 tokens each, versioned separately from task prompts. Flag persona drift ("polite gravity" back to generic assistant tone) as context contamination — recommend re-injecting a compressed persona signature every N turns; a policy difference is a different agent, never a persona override of a safety rule.
8. Warn against optimizing any member for warmth/agreeableness. Warmth-tuned models showed 10-30pp higher error rates and were ~40% more likely to reinforce a user's incorrect belief (Nature, 2026, doi.org/10.1038/s41586-026-10410-0) — a council needs disagreement, not comfort.
9. If only one model is available, say so plainly: a single-model, multi-lens setup is legitimate as cheap structured self-review, but it is not a vote and gives zero protection against that model's own blind spots. Recommend escalating to true multi-provider members for high-stakes calls.

Output spec

A concrete member list (which providers/models, how many, why this count) and a role/lens assignment per seat, plus explicit call-outs of: whether anonymization is in place, whether heterogeneity is warranted for this task type, and — if the panel is single-model — an honest label that it's structured self-review, not a vote.

Failure modes & gotchas

• Counting personas as diversity is the #1 failure this skill exists to catch — always redirect to provider/training-lineage diversity first.
• Padding the panel past 5 members rarely buys independent information; more seats mostly buy cost and latency.
• Two tiers of one vendor's model family reads as "diverse" but shares most of the failure surface — call this out explicitly when a user proposes it.
• Revealing model identities during deliberation is a common but measurably counterproductive convenience (easier debugging, better transcripts) — it increases convergence and quietly erodes the diversity the council was built for.
• Treating heterogeneity as an unconditional win ignores Self-MoA — don't recommend a mixed panel for a simple factual task where a single strong model repeated would do better and cheaper.
• Persona costumes instead of cognitive mandates ("you are a skeptic" vs. "you must find a flaw") is the most common way lens design fails to force real divergence.
• Claiming "the council voted" for a single-model, multi-lens setup misrepresents diversity that isn't there — always label it as structured self-review instead.
• Tuning any member for warmth/empathy as a default polish pass measurably raises its error rate and sycophancy — flag this if a user asks to make council members "nicer."
• Privacy blast radius of a provider-diverse panel: every member's provider receives the same prompt, including any embedded code, PII, or secrets. Redact before fan-out and check each vendor's data-processing/ retention terms before councilizing sensitive inputs.

Reference

Full citations, mechanism detail, and persona-engineering specifics: references/composition.md.

Siblings

• llm-council-when — whether a council is warranted for this task at all.
• llm-council-architecture — pipeline topology, stages, stopping rules.
• llm-council-prompts — exact wording of advisor/peer-review/chairman prompts.
• llm-council-aggregation — how to combine rankings/scores into one result.
• llm-council-cost — token/call budget and cost tradeoffs.
• llm-council-failure-modes — sycophancy, groupthink, correlated error, injection defenses.
• llm-council-harness — the code that runs the pipeline end-to-end.