This Skill Makes Claude BRUTALLY Honest About Your Ideas thumbnail

This Skill Makes Claude BRUTALLY Honest About Your Ideas

Brad | AI & Automation · ~7 min
This Skill Makes Claude BRUTALLY Honest About Your Ideas thumbnail
Claude Skills Focus Groups Customer Research Sub-Agents Sales Calls → Avatars

🤥 The Yes-Man Problem 0:00

AI is the world's biggest yes-man. No matter how good or bad your idea is, it will justify exactly what you want to hear. It validates, it encourages, it cheers — and that's a dangerous default when you're making real business decisions.

Brad built a skill that solves this by making Claude genuinely honest. The approach: clone your real customers into AI agents and let them react to your ideas the way actual people would — with indifference, skepticism, or enthusiasm depending on what you show them.

Core insight: Instead of asking one AI for feedback (which always says yes), spawn multiple agents modeled on real customers who each react independently.

🎯 Running a Focus Group 0:57

Type /focus-group and paste anything you want tested: a landing page, an offer, an email, a price point. The skill spawns separate sub-agents for each seat on your custom focus panel.

  • Each agent represents a composite avatar built from your real customer data
  • Agents run simultaneously but isolated — Marcus has no idea what Dana said
  • No groupthink, no anchoring bias — each reaction is genuinely independent
  • Works with any content format: text, URLs, screenshots, pricing tables
Key design choice: Isolation between agents prevents the cascade effect where one strong opinion shapes everyone else's — exactly what kills real focus groups too.

🔄 Two-Round Reaction System 1:25 1:49

Round 1 — Quick gut check: Did this get their attention? The system mimics how real people engage with content:

  • If they'd skim it and move on, you get a two-line brush-off like a real person would give
  • Only strong reactions (positive or negative) get full explanations
  • This filters out the polite noise that makes normal AI feedback useless

The Moderator: A separate moderator agent reads all reactions and finds the sharpest disagreements. Critically, it adds no opinions of its own — it's purely an observer identifying conflict.

Round 2 — Debate: Only the disagreeing agents are called back, and each sees the opposing argument. This tests whether they'd genuinely change their mind when confronted with a counter-position.

Why this works: Two rounds simulate the real dynamic where first impressions harden or soften when challenged. You learn which objections are surface-level vs. deeply held.

📊 Actionable Readout 2:09

After both rounds, you get a 30-second summary: what they said, what's missing. Every verdict is cited from real customer quotes — not AI opinions.

  • Consensus view: Where the panel agrees (and where they don't)
  • Cited verdicts: Every claim backed by actual words from your customer transcripts
  • The killer section: "What would change the mind of each holdout?"
💡 The killer insight: Instead of just telling you "3 out of 5 didn't like it," the readout tells you exactly what you'd need to change to convert each skeptic. This turns vague feedback into a concrete action list.

After the readout, you can grill any agent directly — ask follow-up questions, probe deeper into their objections, or test revised messaging on the spot.

🧬 How Avatars Are Built 2:33

On first run, the skill reads your entire sales call library. A fleet of sub-agents each processes a few transcripts, extracting:

  • Exact words customers use to describe their problems
  • Every objection raised during the call
  • Price reactions — how they responded to pricing
  • Buying decision stakeholders — who else is involved
  • Word-for-word quote bank — verbatim language for each person

The system then groups real people by how they buy to create composite avatars. The critical rule:

Quality gate: Every avatar needs at least 3 real people behind it. The system never pads the panel with thin avatars. You can rename, merge, or cut seats to shape your panel.

🧠 Persistent Memory & Evolution 4:01

Each avatar gets a full dossier saved as markdown:

  • Who they are and how they buy
  • Complete objection map
  • 30+ real quotes from actual customers
  • Coverage map showing what data backs each trait

Memory is persistent across sessions. Agents remember past decisions and never contradict themselves. A position file gets updated after every session to track evolving stances.

Living panel: New sales calls automatically refresh dossiers. If new calls reveal customers that don't fit existing avatars, the system proposes a new seat. Your panel evolves with your market — it's never stale.

🛠️ Three Use Cases 4:35

1. Testing Messaging & Positioning

  • Landing pages, emails, hooks, pricing — iterate before anything goes live
  • Get reactions from customer archetypes rather than guessing

2. Validating Concepts Before Building

  • Ask the panel if the need is even real before you invest time
  • Surface objections you haven't thought of from people who match your buyers

3. Post-Mortem on Failures

  • Launch email got no replies? Run it through the panel
  • Get failure causes with receipts: who scrolled past and why, which objection triggered, what would change their mind
Pattern: Use case 3 is often the most valuable — it turns vague "it didn't work" into specific "here's what broke and here's what would fix it."

Setup 5:38

2 minutes to install. Works on Claude Code, Codex, Cursor, and regular Claude.

Two commands: add the marketplace, then install the focus-group skill. Completely free.

First run asks where your sales data lives:

  • Local transcript folder (recommended) — point it at your call recordings/transcripts
  • MCP connection — Fireflies, Google Drive, or other integrations

The system reads your calls, finds avatars, asks you to confirm the panel — and then you have a standing focus group forever.

Get started: Get the Skill →

🎯 Key Takeaways

  • AI is a yes-man by default — this skill forces honest customer reactions
  • /focus-group spawns isolated sub-agents based on composites of your REAL customers
  • Two-round system: gut check → moderator finds disagreements → only dissenters debate
  • Every verdict cited from actual customer quotes, not AI opinions
  • The killer insight: "What would change the mind of each holdout?"
  • Avatars built from sales call transcripts — needs at least 3 real people per seat
  • Persistent memory: agents remember past decisions, never contradict themselves
  • Panel evolves: new calls refresh dossiers, can propose new seats
  • 3 use cases: test messaging, validate concepts, post-mortem failures
  • Free skill, 2-minute install, works on Claude Code / Codex / Cursor