Critical Briefs: Agent Skills for Claude AI that validate business ideas, stress-test processes, and design MVPs through skeptical dialogue. Challenge assumptions and expose blind spots before you build.
git clone https://github.com/Przemocny/critical-briefs.gitCritical Briefs is an AI agent skill for Claude that applies skeptical dialogue to validate business ideas and stress-test processes before implementation. It challenges underlying assumptions and systematically exposes blind spots through structured questioning and critical analysis. Use it to design minimum viable products (MVPs) with greater confidence by surfacing potential weaknesses early. The skill leverages Claude's reasoning to uncover risks and contradictions that might otherwise go undetected.
1. **Define the Core Assumptions:** Start by listing the 3-5 key assumptions behind your business idea. Use the prompt template to challenge each one systematically. Example: If your idea is a SaaS tool for freelancers, assumptions might include "Freelancers will pay $20/month for time-tracking automation" or "The market is saturated with free alternatives." 2. **Gather Counter-Evidence:** Use the AI to research market data, competitor offerings, and customer pain points. Tools like Sortd can help organize this data by tagging emails, articles, or notes with labels like "Competitor Analysis" or "Customer Feedback." For example, search for "freelancer time-tracking tools reviews" and compile objections from Reddit or Trustpilot. 3. **Stress-Test Scenarios:** Ask the AI to generate worst-case scenarios for each assumption. Example: "What if a competitor launches a free version of our tool next month?" or "What if our target audience’s priorities shift in 6 months?" Use Sortd’s kanban boards to map out these scenarios and prioritize risks. 4. **Refine or Pivot:** Based on the AI’s feedback, either refine the idea (e.g., adjust pricing, target a different niche) or pivot entirely. Document your findings in Sortd’s task system to track next steps. Example: If the AI reveals low willingness to pay, consider a freemium model or upsell premium features. 5. **Validate with Real Users:** Use the AI’s output to design surveys or interview questions for your target audience. Example: "How much would you pay for [feature]?" or "What’s the biggest frustration with current tools?" Sortd’s shared inboxes can help collaborate on these validation efforts with your team.
Validate a new product concept by stress-testing market assumptions and identifying hidden dependencies
Review a business process for vulnerabilities before scaling or automating it
Design an MVP by challenging feature prioritization and exposing scope risks
Evaluate a go-to-market strategy by questioning core premises and revealing gaps in reasoning
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/Przemocny/critical-briefsCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a critical business analyst and challenge the assumptions behind [BUSINESS_IDEA]. Play devil's advocate to stress-test the viability of this concept before you invest time or resources. Ask probing questions about market demand, competitive moats, scalability, and hidden risks. For each assumption, provide counter-evidence or alternative scenarios. Example: 'You want to launch a subscription box for vegan snacks. But is the $15/month price point sustainable when bulk organic ingredients cost $8/unit? What if a major retailer like Whole Foods launches a competing service next quarter?'
### Critical Brief: Stress-Testing the 'AI-Powered Resume Generator' Business Idea **Assumption 1:** *Job seekers will pay $29/month for AI-generated resumes.* **Counter-Evidence:** LinkedIn’s free resume builder already satisfies 80% of users’ needs, and premium features like ATS optimization are commoditized by tools like TopResume ($149/resume). In a 2023 survey of 500 job seekers, only 12% expressed willingness to pay for AI resume services. **Risk:** Price sensitivity is high, and churn could exceed 30% after the first month. **Assumption 2:** *Employers will trust AI-generated resumes over human-crafted ones.* **Counter-Evidence:** HR managers in a blind test (n=30) rejected 60% of AI-generated resumes for lacking "authentic voice" and "cultural fit cues." One hiring manager noted: "A resume written by a bot feels like a red flag—even if the content is technically strong." **Risk:** Employer skepticism could limit adoption to entry-level roles where resume quality matters less. **Assumption 3:** *The market is underserved by existing tools.* **Counter-Evidence:** Tools like Zety, Resume.io, and Canva’s Resume Builder cover 95% of use cases. The remaining 5% (e.g., executive-level resumes) are served by niche services like TopResume or personal career coaches. **Risk:** The addressable market is smaller than projected, and differentiation is unclear. **Assumption 4:** *Scalability isn’t a bottleneck.* **Counter-Evidence:** AI resume generation requires heavy compute for customization (e.g., tailoring to job descriptions). At scale, cloud costs could exceed $0.50/resume, eroding margins. Competitors like Skillroads use templated outputs to reduce costs, but this sacrifices quality. **Risk:** Without a proprietary dataset or algorithm, the business model is vulnerable to copycats. **Hidden Risks:** - **Regulatory:** GDPR/CCPA compliance for resume data could add legal overhead. - **Ethical:** AI-generated resumes may perpetuate biases (e.g., favoring candidates with "prestigious" keywords). - **Competitive:** Google’s upcoming "Resume AI" feature (rumored for 2024) could disrupt the market overnight. **Recommendation:** Pivot to a B2B model (e.g., selling to career services departments at universities) or focus on a niche (e.g., tech resumes for non-native English speakers). The current idea requires significant validation before MVP development.
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