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Ceo council

Skill serejaris/personal-corp-skills/skills/ceo-council

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 spotsFrom its SKILL.md

Install
npx -y skills add serejaris/personal-corp-skills --skill ceo-council

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • runs commandsInstructs the agent to run 1 command, including `git log --oneline -10`.

SKILL.md

5.5 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

CEO Council — Independent Strategic Analysis

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.

Critical: How to Launch Experts

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.

Step 1: Scan Project Context

Before suggesting experts, understand the project:

  1. Read CLAUDE.md (or README.md if absent)
  2. Scan .claude/rules/ for domain context
  3. Glance at top-level file structure

Based on findings, generate 4-6 expert roles tailored to THIS project. Roles must reflect the project's actual domain, challenges, and stage.

Step 2: Assemble the Council

MANDATORY: Ask the user before proceeding. Do not pick roles yourself.

Use AskUserQuestion with multiSelect: true:

  • Show 4-6 role options with short descriptions of their focus
  • User can always pick "Other" to define custom roles
  • Minimum 2 experts. If user picks 1, suggest adding one more for productive disagreement

Role Examples by Project Type

Don't copy these — generate fresh roles based on actual project context:

Project TypeTypical Roles
SaaSHead of Engineering, Head of Product, Head of Growth, CFO, UX Researcher
Open SourceCommunity Manager, Technical Architect, DevRel, Security Advisor
Content / MediaContent Strategist, Audience Analyst, Monetization Expert, Distribution Expert
EdTechCMO, CFO, CPO, COO, Growth Advisor
E-commerceHead of Supply Chain, Marketing Director, CTO, Customer Experience Lead
Agency / ConsultingSales Director, Delivery Lead, Talent Manager, CFO

Step 3: Gather Current Data

Collect project state to feed all experts. Stay focused on what's relevant:

Read:

  • Key metrics/data files identified during context scan
  • Strategy and planning documents
  • Recent decisions or changes (git log --oneline -10)
  • Previous council analyses (if any)

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.

Step 4: Generate Expert Prompts

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:

  • Each expert gets the SAME data block — prepare it once, reuse
  • Focus areas must be specific to the project, not generic
  • Include a personality instruction (contrarian, pragmatic, data-driven)
  • Mention project constraints the expert should know

Step 5: Execute

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.

Step 6: Synthesize

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 Results

Save to a logical location:

  • docs/council-[DATE].md — default
  • Or project-specific path if context suggests one

Common Mistakes

MistakeFix
Picking roles without asking userALWAYS use AskUserQuestion first
Using bash to launch expertsONLY use Task tool with subagent_type: "general-purpose"
Giving experts different dataPrepare ONE data block, paste into all prompts
Gathering vanity metricsFocus on project docs, strategy, actual metrics
Too many experts (6+)3-4 is optimal for signal-to-noise
Skipping synthesisThe synthesis IS the value — never skip

What ships with it: 3 files

463.0 KB alongside SKILL.md

assets/

Gives 0 of the 12 instructions most product growth skills give in ~1.2k tokens

Counted across 694 of the 879 authors here whose files we hold, read 2026-09-06

  • Check for product marketing context firstin 49 of 694, across 20 files
  • Validate the why before building featuresin 18 of 694, across 4 files
  • Respond to every comment in real-timein 17 of 694, across 6 files
  • Structure launch marketing across three channel typesin 16 of 694, across 4 files
  • Recruit early users one-on-onein 13 of 694, across 2 files
  • Ask one question at a timein 13 of 694
  • Rank features using ICE scoringin 12 of 694, across 3 files
  • Identify primary conversion goalin 11 of 694, across 3 files
  • Identify traffic contextin 11 of 694, across 3 files
  • Evaluate headline effectivenessin 11 of 694, across 3 files
  • Check visual hierarchy and scannabilityin 11 of 694, across 3 files
  • Run product diagnosticsin 11 of 694, across 3 files

Said here and by no other author read

  • Ask the user before picking roles
  • Prepare the data block once
  • Launch all experts in a single message
  • Synthesize the council results
  • Save results to a logical location

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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