startup-idea-validation▌
vasilyu1983/ai-agents-public · updated Apr 8, 2026
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Systematic validation for testing ideas before building: define hypotheses, collect evidence, score the opportunity, and make a decision you can defend.
Startup Idea Validation
Systematic validation for testing ideas before building: define hypotheses, collect evidence, score the opportunity, and make a decision you can defend.
Operating Principles (2026)
- Prefer decisions over inventories: each dimension ends with
GO / CONDITIONAL / PIVOT / NO-GOand a next action. - Separate evidence quality from confidence: weak evidence cannot justify a high score.
- Pre-register thresholds and stop rules before running experiments (avoid moving goalposts).
- Validate willingness-to-pay and time-to-value early (price is part of the product).
- Calibrate thresholds to the target outcome (venture-scale vs cash-flow business) and business model (B2B SaaS, B2C, marketplace, services).
- Stay safe and ethical: no misrepresentation, respect ToS, and handle customer data with minimization and retention limits.
Intake Checklist (Ask First)
- One-sentence idea + target user + job-to-be-done
- Business model: B2B/B2C, SaaS/usage-based/marketplace/services, ACV/ARPU range
- Geography, constraints (regulated domain, procurement/security requirements, data access)
- Target outcome: venture-scale, profitable small business, or thesis-driven R&D
- Current evidence: interviews, pilots, pre-sales, traffic, competitor list, pricing assumptions
Choose the Right Output
| If the user asks… | Produce… | Use… |
|---|---|---|
| “Validate this idea” / “Is this worth building?” | 9-dimension scorecard + verdict | validation-scorecard.md, go-no-go-decision.md |
| “What’s the riskiest assumption?” | RAT + test plan | riskiest-assumption-test.md, validation-experiment-planner.md |
| “Test my hypothesis” | Hypothesis canvas + experiment design | hypothesis-canvas.md, hypothesis-testing-guide.md |
| “Market size for X” | TAM/SAM/SOM sizing + assumptions table | market-sizing-worksheet.md, market-sizing-patterns.md |
| “Can this be profitable / what’s my runway?” | Unit economics + runway + scenarios | financial-modeling-calculator.md |
| “Should I build X or Y?” | Comparative scorecard + decision memo | validation-scorecard.md, go-no-go-decision.md |
Workflow
- Clarify the target outcome and business model; set default thresholds accordingly.
- Identify the RAT (the assumption that kills the business if wrong).
- Plan the validation ladder: interviews -> smoke test -> concierge/WoZ -> paid pilot.
- Run the cheapest falsifiable test first; pre-register PASS/FAIL thresholds and stop rules.
- Score all 9 dimensions using evidence; downgrade scores when evidence is weak.
- Produce a decision memo: verdict, why, what would change the decision, and the next smallest reversible step.
9-Dimension Scorecard
| Dimension | Weight | What it measures |
|---|---|---|
| Problem severity | 15% | Urgency, cost of inaction, current workarounds |
| Market size | 12% | Sufficient demand for the target outcome |
| Market timing | 10% | Clear “why now” and tailwinds |
| Competitive moat | 12% | Defensibility over time |
| Unit economics | 15% | Profit path (incl. payback and margins) |
| Founder-market fit | 8% | Access, expertise, and execution capability |
| Technical feasibility | 10% | Buildability, dependencies, constraints |
| GTM clarity | 10% | ICP, channels, motion, first customers |
| Risk profile | 8% | What can kill it and likelihood |
Verdict thresholds (default):
80–100: GO60–79: CONDITIONAL (validate RAT first)40–59: PIVOT<40: NO-GO
Deep scoring rubrics and calibration live in validation-methodology.md.
Evidence Rules
- Strong evidence is behavioral commitment with cost (time, money, switching, access); weak evidence is opinions and hypotheticals.
- Triangulate important claims across at least two sources (especially market sizing and competitor state).
- Keep an evidence trail: link + capture month; separate “fact” vs “assumption”.
Validation Ladder (Default)
| Step | Goal | Strong signal |
|---|---|---|
| Interviews | Validate the problem and context | Repeated pain with real workarounds and spend |
| Smoke test | Validate demand | Qualified conversion with price shown |
| Concierge/WoZ | Validate workflow value | Users complete the job and return |
| Paid pilot | Validate willingness-to-pay | Paid, renewed, or expanded |
AI / Automation Notes (2026)
If the idea depends on AI (agents, copilots, automation), validate these explicitly:
- Data rights and access: can you legally and reliably access required data?
- Reliability: define success metrics, failure modes, and human fallback; validate on real workflows.
- Cost-to-serve: model inference + retrieval + human-in-the-loop costs in
assets/financial-modeling-calculator.md.
See hypothesis-testing-guide.md for AI-specific experiment patterns.
Integration Points
Receives From
- startup-review-mining - Pain point evidence
- startup-trend-prediction - Market timing inputs
- startup-competitive-analysis - Competitor landscape
Feeds Into
- router-startup - Startup decision routing
- product-management - Validated requirements and roadmap inputs
- startup-business-models - Monetization and packaging decisions
Resources
| Resource | Purpose |
|---|---|
| validation-methodology.md | Scoring rubrics and calibration |
| hypothesis-testing-guide.md | Experiment design and RAT workflows |
| market-sizing-patterns.md | TAM/SAM/SOM methods and pitfalls |
| moat-assessment-framework.md | Defensibility analysis |
| customer-interview-guide.md | Interview methodology, scripts, and analysis |
| landing-page-validation.md | Smoke tests, conversion benchmarks, landing page tools |
| competitive-landscape-assessment.md | Competitive scan, gap analysis, market mapping |
| pivot-framework.md | Pivot triggers, types, decision framework, case studies (Slack, Instagram, Shopify) |
Templates
| Template | Purpose |
|---|---|
| validation-scorecard.md | Full 9-dimension scoring |
| go-no-go-decision.md | Decision memo format |
| hypothesis-canvas.md | Hypothesis definition |
| validation-experiment-planner.md | Experiment planning + thresholds |
| riskiest-assumption-test.md | RAT identification and test design |
| market-sizing-worksheet.md | Sizing worksheet |
| financial-modeling-calculator.md | Runway + scenarios + unit economics |
Data
| File | Purpose |
|---|---|
| sources.json | Curated validation resources |
How to use startup-idea-validation on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add startup-idea-validation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches startup-idea-validation from GitHub repository vasilyu1983/ai-agents-public and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate startup-idea-validation. Access the skill through slash commands (e.g., /startup-idea-validation) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★48 reviews- ★★★★★Aditi Malhotra· Dec 24, 2024
startup-idea-validation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Dhruvi Jain· Dec 20, 2024
Registry listing for startup-idea-validation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Henry White· Dec 20, 2024
startup-idea-validation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Pratham Ware· Dec 16, 2024
startup-idea-validation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Hana Garcia· Dec 12, 2024
Registry listing for startup-idea-validation matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Aditi Chawla· Nov 15, 2024
Solid pick for teams standardizing on skills: startup-idea-validation is focused, and the summary matches what you get after install.
- ★★★★★Oshnikdeep· Nov 11, 2024
startup-idea-validation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Henry Abbas· Nov 11, 2024
We added startup-idea-validation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Hana Johnson· Nov 3, 2024
startup-idea-validation reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Emma Abebe· Oct 22, 2024
I recommend startup-idea-validation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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