Ceo Board

CEO Board Deliberation Skill

Overview

The CEO Board Deliberation skill orchestrates multi-agent reasoning for strategic business decisions. It implements IndyDevDan's innovation of templated agent expertise with persistent learning.

Created: 2026-03-24 Framework: CleanExpo/NodeJS-Starter-V1 Agents: 9 specialized board members with persistent expertise

The Golden Example

To understand how the board works, review this complete example decision:

File: .pi/ceo-agents/briefs/_EXAMPLE-acquisition-offer.md

What it demonstrates:

• How to structure a board brief
• Expected deliberation process
• Agent perspective patterns
• How expertise files are updated
• Output format for decision memos

Use this example to train new team members on the board's decision process.

Quick Start

1. Prepare Your Brief

Copy the template and fill it out:

cp .pi/ceo-agents/briefs/_TEMPLATE.md \
   .pi/ceo-agents/briefs/[YOUR-TOPIC]-[DATE].md

Required sections:

• Situation — What's the context?
• Stakes — What's at risk or possible?
• Constraints — What are our limits?
• Key Questions — What does the board decide?

2. Start the Board

/ceo-begin [YOUR-TOPIC]-[DATE]

The board will:

1. Review your brief
2. Deliberate with all 9 agents
3. Debate and challenge each other
4. Reach a decision
5. Update expertise files with their learning

3. Review the Decision

Find outputs at:

.pi/ceo-agents/
├── memos/[TOPIC]-[DATE].md        # Decision summary
├── conversations/[TOPIC]-[DATE].json # Full debate transcript
└── artifacts/[TOPIC]-[DATE].svg     # Visual arguments

The 9 Board Members

1. CEO

Perspective: Holistic business lens, execution reality Questions: Can we execute this? Does it align with strategy? How do we communicate it? Expertise File: .pi/ceo-agents/expertise/ceo.md

2. Revenue Agent

Perspective: Growth, unit economics, expansion Questions: How does this impact MRR? Is it capital-efficient? What's the TAM? Expertise File: .pi/ceo-agents/expertise/revenue.md

3. Product Strategist

Perspective: Feature prioritization, user value, roadmap Questions: Does this delight users? How does it fit the roadmap? What's the UX impact? Expertise File: .pi/ceo-agents/expertise/product-strategist.md

4. Technical Architect

Perspective: System design, feasibility, technical debt Questions: Is this technically sound? What's the implementation complexity? Scalability concerns? Expertise File: .pi/ceo-agents/expertise/technical-architect.md

5. Contrarian

Perspective: Risk identification, blindspots, second-order effects Questions: What could go wrong? What are we not seeing? When should we say "no"? Expertise File: .pi/ceo-agents/expertise/contrarian.md

6. Compounder

Perspective: Long-term leverage, compound effects, platform thinking Questions: What compound effects does this create? Will this give us an unfair advantage over time? Expertise File: .pi/ceo-agents/expertise/compounder.md

7. Custom Oracle

Perspective: Domain-specific expertise (customize for your industry) Questions: What do domain experts know about this? Regulatory/compliance concerns? Expertise File: .pi/ceo-agents/expertise/custom-oracle.md

8. Market Strategist

Perspective: Competitive positioning, market timing, go-to-market Questions: How does this position us competitively? Is timing right? Market adoption risk? Expertise File: .pi/ceo-agents/expertise/market-strategist.md

9. Moonshot

Perspective: Radical innovation, asymmetric bets, breakthrough possibilities Questions: Is there a bigger opportunity here? What's the moonshot play? Expertise File: .pi/ceo-agents/expertise/moonshot.md

How Agent Expertise Works

Each agent has a persistent expertise file that evolves:

Initial State

ceo.md
├── Role Description — What this agent brings
├── Core Perspectives — Their lens on problems
├── Decision History — Table of all decisions they've participated in
└── Learning Notes — What they've learned over time

After Each Deliberation

Agents update their files with:

1. Decision Record — Topic, position taken, outcome
2. Learning — What they learned from the debate
3. Pattern Recognition — How this fits with past decisions
4. Accuracy Tracking — How did their prediction fare?

Example Update

| Date | Topic | Position | Outcome | Notes |
|------|-------|----------|---------|-------|
| 2026-03-24 | Acquisition Offer | "Pursue, but negotiate harder" | TBD | Concerned about valuation, team retention |

Deliberation Quality Metrics

The board improves over time. Track:

1. Decision Quality — How well do decisions age?
2. Agent Agreement — Which agents tend to align?
3. Contrarian Accuracy — How often does the Contrarian spot real risks?
4. Compounder Insight — Which compound bets pay off?
5. Speed — How quickly does the board reach consensus?

Best Practices

For Brief Writers

1. Be Specific — Vague situations lead to weaker deliberations
2. Include Data — Concrete metrics help agents reason better
3. State Constraints — Be honest about limitations
4. Ask Real Questions — Don't lead the board toward a predetermined answer

For Board Users

1. Trust the Process — Diverse perspectives catch blindspots
2. Review Dissent — The Contrarian's concerns often matter most
3. Track Predictions — Come back and see how the board's predictions fared
4. Improve Expertise — Manually update agent files with industry insights

For Maintenance

1. Keep Expertise Current — Update agent files as patterns emerge
2. Archive Decisions — Keep decision history for reference
3. Learn from Outcomes — Track prediction accuracy
4. Customize Agents — Adjust the Custom Oracle for your industry

File Structure

.pi/
├── README.md                           # PI workspace overview
├── ceo-agents/
│   ├── briefs/
│   │   ├── _TEMPLATE.md                # Brief template (copy this)
│   │   ├── _EXAMPLE-acquisition-offer.md # Golden example
│   │   └── [TOPIC]-[DATE].md           # Your briefs go here
│   ├── deliberations/
│   │   └── [TOPIC]-[DATE].json         # Deliberation records
│   ├── memos/
│   │   └── [TOPIC]-[DATE].md           # Decision memos
│   ├── conversations/
│   │   └── [TOPIC]-[DATE].json         # Full transcripts
│   ├── artifacts/
│   │   └── [TOPIC]-[DATE].svg          # Visual arguments
│   └── expertise/
│       ├── ceo.md
│       ├── revenue.md
│       ├── product-strategist.md
│       ├── technical-architect.md
│       ├── contrarian.md
│       ├── compounder.md
│       ├── custom-oracle.md
│       ├── market-strategist.md
│       └── moonshot.md
└── shared/
    └── context/
        └── _TEMPLATE.md                # Shared business context

Integration with CleanExpo

The CEO Board integrates with your workflow:

• Briefs can reference GitHub issues or milestones
• Memos can be published as announcements
• Artifacts can embed in documentation
• Expertise informs prioritization and resource allocation

Related Commands

• /hey-claude — Ask Claude to explain board decisions
• /swarm-audit — Audit decision-making patterns
• /generate-route-reference — Keep documentation in sync

Credits

Inspired by: IndyDevDan's agent scaling insights Insight: "If you template your engineering, your agents can do exactly what you did. This is the big advantage. When you're not creating prescriptions, workflows, and systems for your agents to repeat, you miss out on all the true leverage."

Last Updated: 2026-03-24 Status: Active Maintainer: CleanExpo Team