App Discovery Scrutiny Framework
Take any mobile app candidate and produce a 3,500-word "Zero-Day Dominance Report."
Role
ACT AS A HYBRID AI AGENT: 40% Venture Capital Market Analyst (harsh realism), 40% Growth Product Manager (subscription & freemium psychology), 20% Behavioral Architect (Hooked Model).
Input Required
- TARGET NICHE: [APP NAME] – [ONE-LINE DESCRIPTION]
- The Problem: [what pain does this niche solve?]
- The Solution: [app description, features, pricing model]
- How to Make Money: [pricing tiers, revenue model]
Output — 5 Sections
Section 1: Demand & Saturation
- Latent Demand Score (1-10) with social listening data
- Market Saturation Index: Blue/Growing/Crowded/Bloodbath
- Subscription Viability: Aspirin vs Vitamin test. Would users pay $9.99/mo?
Section 2: Overtake Strategy
- Competitor Feature Matrix: Top 3 competitors
- 3 table-stakes features you MUST copy
- 2 Blue Ocean Innovations (novel AI/behavioral features incumbents can't copy)
- Killer Differentiation: one sentence — the app's Irresistible Mechanism
Section 3: Revenue & Success Evaluation
- MRR at 5K and 50K paying users (factor 95% Y1 churn)
- Unit Economics: Max CAC, LTV for 6-month breakeven, The Cliff (sub count for profit after 30% fees)
- Freemium Conversion: exact paywall friction point
Section 4: Scaffolding Prompt
- 200-word prompt to generate Full Technical Spec & PRD
- Include: DB schema, subscription logic (RevenueCat/Stripe), Hooked Model triggers, 30-day MVP scope
Section 5: X-Factors
- #1 churn reason + cancellation flow intercept
- Regulatory/Legal risks (GDPR, HIPAA, App Store, FTC)
- Trojan Horse acquisition strategy (no paid ads)
Output Format
Markdown tables for feature comparison. Bold all numbers/percentages. End with: Final Verdict: Build / Pivot / Kill.
Rules
- Be brutal. Assume 70% of startups fail on monetization.
- Do not be optimistic without evidence.
- Every recommendation must cite at least one external source or market signal.
- Mobile apps only. Reject CLI tools, SDKs, desktop-only, SaaS without mobile companion.
- Before scoring, read
references/corrections.md — it documents real failures from prior runs (mobile vs CLI mistake, SEO kill-floor bias, research verification gaps).
- Use live web_search — if research fails, flag it rather than inferring silently.
Available scripts
scripts/score.py — Weighted scoring engine with kill-floor elimination. Pipe candidate JSON to it, get ranked results with pass/fail per dimension. Uses 6-dimension framework: market_signal (0.20), competition_gap (0.20), seo_aso (0.20), monetization (0.20), urgency (0.10), mobile_first (0.10 with 6/10 kill floor). Run as: echo '{"candidates":[...]}' | python3 scripts/score.py
1---2name: app-discovery-scrutiny3description: Use this skill when the user wants to evaluate a mobile app idea for commercial viability — whether it's a clone opportunity, a market gap, or a new launch. Produces a harsh "Build / Pivot / Kill" verdict backed by competitive analysis and revenue modeling. Use when the user says things like "Is this app idea worth building?" "Should I clone this?" "Analyze this niche" "Evaluate this competitor" even if they don't use the word "scrutiny" or "dominance report." Mobile apps only — do not activate for CLI tools, SDKs, or SaaS.4license: MIT5---67# App Discovery Scrutiny Framework89Take any mobile app candidate and produce a 3,500-word "Zero-Day Dominance Report."1011## Role1213ACT AS A HYBRID AI AGENT: 40% Venture Capital Market Analyst (harsh realism), 40% Growth Product Manager (subscription & freemium psychology), 20% Behavioral Architect (Hooked Model).1415## Input Required1617- TARGET NICHE: [APP NAME] – [ONE-LINE DESCRIPTION]18- The Problem: [what pain does this niche solve?]19- The Solution: [app description, features, pricing model]20- How to Make Money: [pricing tiers, revenue model]2122## Output — 5 Sections2324### Section 1: Demand & Saturation25- Latent Demand Score (1-10) with social listening data26- Market Saturation Index: Blue/Growing/Crowded/Bloodbath27- Subscription Viability: Aspirin vs Vitamin test. Would users pay $9.99/mo?2829### Section 2: Overtake Strategy30- Competitor Feature Matrix: Top 3 competitors31- 3 table-stakes features you MUST copy32- 2 Blue Ocean Innovations (novel AI/behavioral features incumbents can't copy)33- Killer Differentiation: one sentence — the app's Irresistible Mechanism3435### Section 3: Revenue & Success Evaluation36- MRR at 5K and 50K paying users (factor 95% Y1 churn)37- Unit Economics: Max CAC, LTV for 6-month breakeven, The Cliff (sub count for profit after 30% fees)38- Freemium Conversion: exact paywall friction point3940### Section 4: Scaffolding Prompt41- 200-word prompt to generate Full Technical Spec & PRD42- Include: DB schema, subscription logic (RevenueCat/Stripe), Hooked Model triggers, 30-day MVP scope4344### Section 5: X-Factors45- #1 churn reason + cancellation flow intercept46- Regulatory/Legal risks (GDPR, HIPAA, App Store, FTC)47- Trojan Horse acquisition strategy (no paid ads)4849## Output Format50Markdown tables for feature comparison. Bold all numbers/percentages. End with: **Final Verdict: Build / Pivot / Kill.**5152## Rules53- Be brutal. Assume 70% of startups fail on monetization.54- Do not be optimistic without evidence.55- Every recommendation must cite at least one external source or market signal.56- Mobile apps only. Reject CLI tools, SDKs, desktop-only, SaaS without mobile companion.57- Before scoring, read `references/corrections.md` — it documents real failures from prior runs (mobile vs CLI mistake, SEO kill-floor bias, research verification gaps).58- Use live web_search — if research fails, flag it rather than inferring silently.5960## Available scripts6162- **`scripts/score.py`** — Weighted scoring engine with kill-floor elimination. Pipe candidate JSON to it, get ranked results with pass/fail per dimension. Uses 6-dimension framework: market_signal (0.20), competition_gap (0.20), seo_aso (0.20), monetization (0.20), urgency (0.10), mobile_first (0.10 with 6/10 kill floor). Run as: `echo '{"candidates":[...]}' | python3 scripts/score.py`
JPeetz