Use when needing strategic project analysis from multiple independent expert perspectives. Triggers on business decisions, growth strategy, product direction, competitive analysis, or any situation where diverse C-level opinions reduce blind spots
Launch parallel sub-agents as isolated C-level experts. Each analyzes the same project data from their perspective. No coordination between experts — isolation produces genuine diversity of opinion. Then synthesize consensus and disagreements.
MUST use the Task tool with subagent_type: "general-purpose" and model: "opus".
Task(
subagent_type: "general-purpose",
model: "opus",
prompt: "<expert prompt with data>",
description: "CFO analysis"
)
DO NOT use bash, shell scripts, or background commands to launch experts. They will fail.
Launch all experts in a single message with multiple Task tool calls for true parallelism.
Before suggesting experts, understand the project:
CLAUDE.md (or README.md if absent).claude/rules/ for domain contextBased on findings, generate 4-6 expert roles tailored to THIS project. Roles must reflect the project's actual domain, challenges, and stage.
MANDATORY: Ask the user before proceeding. Do not pick roles yourself.
Use AskUserQuestion with multiSelect: true:
Don't copy these — generate fresh roles based on actual project context:
| Project Type | Typical Roles |
|---|---|
| SaaS | Head of Engineering, Head of Product, Head of Growth, CFO, UX Researcher |
| Open Source | Community Manager, Technical Architect, DevRel, Security Advisor |
| Content / Media | Content Strategist, Audience Analyst, Monetization Expert, Distribution Expert |
| EdTech | CMO, CFO, CPO, COO, Growth Advisor |
| E-commerce | Head of Supply Chain, Marketing Director, CTO, Customer Experience Lead |
| Agency / Consulting | Sales Director, Delivery Lead, Talent Manager, CFO |
Collect project state to feed all experts. Stay focused on what's relevant:
Read:
Skip: GitHub traffic stats, stargazer counts, clone data, contributor lists — these are vanity metrics, not strategic data.
All experts must receive identical data context. Prepare the data block ONCE, then paste it into each expert prompt.
For each selected expert, create a prompt with the SAME data block:
You are the [ROLE] for [PROJECT NAME]. Analyze the data below from a [DOMAIN] perspective.
Focus on:
- [3-6 specific focus areas relevant to role and project]
Data:
[CURRENT PROJECT DATA — identical for all experts]
[Role-specific instruction: "show the math", "be the contrarian", "prioritize by effort/impact", etc.]
Respond in the same language as the data provided.
Rules:
Launch ALL selected experts in one message using multiple Task tool calls:
# In a single response, call Task for each expert:
Task(subagent_type: "general-purpose", model: "opus", prompt: "<CFO prompt>", description: "CFO analysis")
Task(subagent_type: "general-purpose", model: "opus", prompt: "<CPO prompt>", description: "CPO analysis")
Task(subagent_type: "general-purpose", model: "opus", prompt: "<CTO prompt>", description: "CTO analysis")
Wait for all experts to return results before proceeding to synthesis.
Do not skip this step. The synthesis is the entire value of the council.
After all experts report, create a synthesis document:
# Council Session: [DATE]
## Council Members
[List of selected experts and their focus]
## Context
[Current metrics/state snapshot — brief]
## [Expert 1 Name]
[Key findings and recommendations]
## [Expert 2 Name]
[Key findings and recommendations]
## Consensus (all agree)
1. ...
2. ...
## Disagreements
| Expert | Position | Argument |
|--------|----------|----------|
| ... | ... | ... |
## Decisions
_To be filled after discussion._
Save to a logical location:
docs/council-[DATE].md — default| Mistake | Fix |
|---|---|
| Picking roles without asking user | ALWAYS use AskUserQuestion first |
| Using bash to launch experts | ONLY use Task tool with subagent_type: "general-purpose" |
| Giving experts different data | Prepare ONE data block, paste into all prompts |
| Gathering vanity metrics | Focus on project docs, strategy, actual metrics |
| Too many experts (6+) | 3-4 is optimal for signal-to-noise |
| Skipping synthesis | The synthesis IS the value — never skip |