csuite

Classify executive requests into decision types and generate structured recommendations.

415|44|Updated Mar 18, 2026
One-click install
npx skills add https://github.com/notque/vexjoy-agent --skill csuite
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: csuite
Source: https://github.com/notque/vexjoy-agent/tree/main/skills/business/csuite
Command: npx skills add https://github.com/notque/vexjoy-agent --skill csuite

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

csuite helps you turn vague executive questions (strategy, technology choice, growth plans, competitive positioning, or go/no-go evaluation) into a single, decision-ready recommendation backed by structured analysis and explicit trade-offs.

Core Features & Use Cases

  • Executive mode detection: Determines whether your request is primarily STRATEGY, TECHNOLOGY, GROWTH, COMPETITIVE, or EVALUATION, and routes you to the correct framework.
  • Evidence-based decision frameworks: Applies a mode-specific workflow (e.g., CEO FRAME→ANALYZE→DECIDE, CTO SCOPE→EVALUATE→RECOMMEND, CMO ASSESS→STRATEGIZE→PLAN, Competitive MAP→ANALYZE→POSITION, Evaluation SCOPE→EVALUATE→VERDICT).
  • Decision gates and revisit triggers: Forces options (2–4), separates facts from assumptions, quantifies where possible, and sets explicit conditions for revisiting the decision.

Quick Start

Use the csuite skill to evaluate a proposed investment decision and produce a one-sentence recommendation with options, assumptions, and a revisit trigger.

Frequently Asked Questions about csuite

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure executive decision-making for a go no-go evaluation?

Executive decision-making for a go no-go evaluation requires scoping options, evaluating feasibility, effort, and ROI, and delivering a gated verdict with explicit confidence levels and revisit conditions. You must separate facts from assumptions and quantify outcomes where possible to ensure evidence-focused analysis.

What is the best way to evaluate a build vs buy decision for a new technology stack?

Evaluating a build vs buy decision requires scoping your technology requirements, evaluating vendor and tech stack options against specific criteria, and recommending a selection. This structured trade-off evaluation labels facts versus assumptions and uses matrix scoring to generate a confident, evidence-backed selection.

How does competitive intelligence mapping work for differentiation strategy?

Competitive intelligence mapping works by analyzing the competitive landscape to identify differentiation opportunities. It involves mapping market positions, analyzing competitor strengths and weaknesses, and defining a strategic positioning framework to guide executive decisions on market entry or product differentiation.

Can I use a structured framework for growth channel planning and 90-day execution design?

Yes, growth channel planning uses a structured framework to assess potential channels, strategize resource allocation, and plan a 90-day execution design. This approach forces explicit options, quantifies expected growth outcomes, and sets first actions and revisit triggers to ensure accountable execution.

When do I need to separate facts from assumptions in strategy framework analysis?

You must separate facts from assumptions in strategy framework analysis whenever you are making investment or trade-off evaluations. This evidence-focused practice ensures that executive recommendations are grounded in verified data, quantifies ROI where possible, and establishes explicit conditions for revisiting the decision.

What are the limitations of using automated frameworks for executive decision support?

Automated executive decision support frameworks require clear input requests to detect the correct mode (strategy, technology, growth, competitive, or evaluation). They cannot generate reliable recommendations without sufficient evidence, rely heavily on explicit options (2-4), and need human validation to confirm fact-versus-assumption labeling and matrix scoring accuracy.