council

name	council
tier	capability
runtime	on-demand
trigger	mention
description	Multi-perspective parallel generation + judge-and-select (a panel of experts 'meets' to deliberate a winner): facing a wide solution space for a design/decision, dispatch N different persona+angle in parallel to produce candidates → multi-lens judge → select-and-synthesize (graft the runners-up's highlights), instead of giving one averaged answer in a single shot. Trigger: multiple options / compare options / best of n / bestof / fanout / council / parallel exploration / multiple perspectives / give me a few options / which option is better / option comparison / explore options / not sure which to pick / domain fork / grounded / anti-happy-path decision / 多个方案 / 对比方案 / 并行探索 / 多视角 / 给我几个选项 / 哪种方案好 / 方案对比 / 拿不准选哪个. Skip: a single clear solution (just do it) / pure execution needing no selection / code review (/review) / root-cause debugging (/investigate).
metadata	{"source":"oh-my-dag","version":"1.1.0","methodology":"diversity>volume + persona conditioning + multi-judge panel + graft runners-up + grounded (market-first + domain-role persona + anti-happy-path axis)"}
disable-model-invocation	true

/council — Multi-Perspective Parallel Generation + Judge-and-Select

When the solution space is wide, a one-shot answer = landing on the mediocre center of the probability distribution. Fanout uses diversified personas to pull generation into different expert regions, multi-lens judge to counter single-judge bias, and select + graft to take the best of each candidate. This is not "resample N times" — that is diminishing-returns volume stacking. The core of fanout is diversity > volume.

When to use

• Architecture/design choices: multiple defensible directions with real tradeoffs (performance vs simplicity vs reversibility).
• Hard-to-reverse decisions: high cost of being wrong, worth adversarial multi-perspective vetting before committing.
• Option-comparison requests: the user explicitly says "give me a few options / which is better / not sure".
• Deep research: a single question needing multiple expert perspectives + sub-angle coverage (use the deep tier).
• Domain-correctness fork (grounded): domains where being wrong is expensive and reality is messy (accounting/legal/ops: data won't reconcile, partial failure, lifecycle-end) + hard to reverse -> use the grounded tier.

When not to use (anti-slop)

• A single clear solution → just do it, don't fan out for ceremony.
• Pure execution (implementing an already-decided plan) → execute, no selection.
• Code review → /review; root-cause debugging → /investigate.

Two tiers

Light tier — /council

Current deliberation context → 3 default lenses produce candidates in parallel → multi-perspective judge scores → cherry-pick synthesis. Suited to a single design choice.

The 3 default lenses (each = persona + angle + sampling modulation):

lens	persona	angle	temp/topP
mvp	pragmatic delivery-oriented engineering lead	smallest viable cut, fastest verification loop, cut the non-core	0.4 / 0.85
risk	senior SRE + security engineer	reason backward from failure modes/boundaries/irreversible points, plug risks first	0.5 / 0.9
first-principles	first-principles thinker	reframe the essence of the problem, question premises, find the simplest structure	0.75 / 0.95

Deep tier — /council deep

L lens × V sub-angle variants → per-lens reduce to a champion → M framing synthesis → K-judge panel + graft → final solution. Each leaf is injected with persona + a high-level domain abstraction framework + ground truth. Suited to foundational / hard-to-reverse decisions (driven by the task's mass: L = the real number of expert perspectives, V = that lens's real number of sub-angles).

Grounded tier — /council grounded (domain fork · structural anti-happy-path)

When to auto-upgrade: a domain-correctness fork (accounting/legal/ops) + real-world messiness + a hard-to-reverse decision. The default lenses (mvp/risk/first-principles) are too generic there — swap in domain roles + an anti-happy-path judging axis + market-first evidence. Four steps (orthogonal to light/deep tiers):

1. Market-first (don't assume): research what competitors / real-world practice actually do first (e.g. via /dag-research) — that becomes the hard-evidence baseline fed to every persona. Keep search queries short and focused.
2. Domain-role personas (not generic lenses): swap to the real roles of the problem — the operator who does this daily / compliance & audit / automation first-principles / lifecycle-end (e.g. year-end close). Each role judges independently in parallel, no peeking, all fed the step-1 evidence baseline (argue from facts, not vibes).
3. Judging axis = anti-happy-path scenarios: explicitly prompt each persona to judge every option against dirty data / concurrency / partial failure / cross-boundary / lifecycle-end / scale-balloon — otherwise personas quietly answer for the happy path too.
4. Judge + graft: check consensus (unanimous = strong signal); pick the champion and graft orthogonal highlights from runners-up. Domain red lines are not delegated to the fleet — council output is input; the lead agent + owner render the final call.

Core discipline (copy these 4, hold them even when fanning out manually)

1. Diversity > volume: within a lens there are V different sub-angles, not the same prompt resampled V times (the latter has diminishing returns).
2. persona conditioning: each leaf is injected with one line of ROLE + perspective/first-principles lens, moving the (weaker) model from the generic region into the expert region — moving probability mass to escape mediocrity. Low temp = faithful, high temp = explore the long tail.
3. multi-judge panel: for foundational decisions a single judge has systemic bias → K different judging dimensions (correctness/simplicity/risk) each score once, adversarial-verify.
4. graft runners-up (graft): not "pick one and discard the rest" — synthesize from the champion, but cherry-pick the runners-up's highlights into it.

Manual fan out (no built-in code path / at scale)

1. Define N genuinely different perspectives (default mvp/risk/first-principles, or task-specific expert roles).
2. Inject persona + angle per perspective, in parallel each producing a complete candidate (independent, no peeking at each other).
3. Judge with ≥2 different lenses each (don't use a single standard).
4. Synthesize from the highest score, graft the unique strengths of the rest → one final solution + tradeoff rationale.

The wrap-up must give: the champion solution + why it won + what was grafted from the runners-up (not a bare N-pick-1 conclusion).