expert-panel

name	expert-panel
description	Run a simulated expert panel on a strategic question — brief 4-8 named experts in parallel, synthesize into consensus table
allowed-tools	Agent, Read, Write, Edit, Glob, Grep, Bash(ls *)

Expert Panel

Run a simulated expert panel review on a strategic question. Each expert is briefed independently via a subagent, responds from their published framework, and the results are synthesized into a consensus table with actionable insights.

When to use

• Evaluating a strategic decision from multiple angles before committing
• Stress-testing a thesis or positioning choice
• The user says "ask the experts", "run a panel", "what would X think", or similar
• A decision has high stakes and benefits from structured disagreement

Process

1. Frame the question — Write one clear question that experts can react to. Include:

   • The context (what exists today, what's being decided)
   • The founder/user's current position or thesis
   • What's at stake (why this matters)
   • Any constraints (timeline, resources, audience)

2. Select 4-8 experts — Choose named experts whose published frameworks are directly relevant. Each expert must bring a distinct lens. Good panel composition covers at least 3 of: positioning, growth/PMF, pricing, moat/competition, DX, brand, network effects, offer design.

   Strong expert choices and their lenses:

   • April Dunford — positioning, category, "Obviously Awesome"
   • Hamilton Helmer — moat, "7 Powers" (barrier + benefit test)
   • Lenny Rachitsky — activation, retention, PMF signals
   • Alex Hormozi — offer clarity, value equation, "$100M Offers"
   • Peter Thiel — monopoly, secrets, 10x, "Zero to One"
   • Clayton Christensen — jobs-to-be-done, "Competing Against Luck"
   • Andrew Chen — network effects, cold start, atomic networks
   • Geoffrey Moore — chasm crossing, beachhead, whole product
   • David Sacks — category creation, cadence, fundability
   • Patrick Campbell — pricing, value metrics, willingness-to-pay
   • Swyx — devrel, developer community, open-source GTM
   • Emily Kramer — messaging hierarchy, developer GTM
   • Sahil Lavingia — minimalist path, profitability, community

3. Brief each expert as a parallel subagent — Launch all agents simultaneously. Each prompt must be self-contained:

   • "You are [Name], author of [Work]."
   • Full context paragraph (the expert has never seen this conversation)
   • The specific question or thesis to evaluate
   • "Your task: [specific evaluation request through their lens]"
   • "Keep response under 300-400 words. Be direct."
   • Use model: "sonnet" for cost efficiency — expert opinions don't need opus

4. Synthesize into a consensus table — When all agents return:

   Expert	Verdict (2-4 words)	Key insight (one sentence)

   Then identify:

   • Unanimous agreements — things every expert said
   • Productive tensions — where experts disagree and why
   • Surprising insights — things that challenge the user's assumptions
   • The one thing — if forced to pick one action, what is it

5. Present to the user — Show each expert's response as a short summary (not the full subagent output), then the consensus table, then tensions and actions.

6. If the user wants to go deeper — Run a second round with the same or different experts. Feed Round 1 conclusions as additional context. This catches experts building on each other's insights.

Rules

• Every expert prompt must be fully self-contained. Subagents have zero conversation context. Explain the full situation in every prompt. Terse prompts produce shallow, generic expert responses.
• Never fabricate expert positions. Each expert's response must be consistent with their actual published framework. If uncertain about their framework, stick to their most well-known concepts.
• Cap responses at 300-400 words per expert. Longer responses dilute the synthesis and waste context. The value is in the verdict, not the essay.
• Always use parallel subagents. Sequential expert calls waste time and create ordering bias. Launch all at once.
• Don't let one expert dominate the synthesis. Weight by insight quality, not by how much the user likes the expert. If the user has expressed preferences about experts, respect them but don't let it bias the analysis.
• Name the disagreements explicitly. A panel where everyone agrees is either right or useless. Surface the tensions — they're where the real insight lives.
• Include the expert count in consensus. "7/12 experts agree" is more useful than "most experts agree."