ai-startup-feasibility-check

Structural feasibility screening for AI startup directions: 13-item AI startup prohibition list, three-axis scoring (substitution risk / leverage direction / unit economics), and a 10-question self-check. Delivers a red / yellow / green diagnostic report.

When to use it

• Deciding whether a specific AI startup direction is worth pursuing
• Judging the structural death risk of an AI project
• Adding a "feasibility self-check" appendix to reports produced by elite-market-researcher / market-analysis

What you get

A four-module structured diagnostic report: prohibition-list hit check (13 items, line by line) → three-axis scoring (substitution risk / leverage direction / unit economics, scored on ±2) → 10-question self-check → verdict and recommendations (with a pre-mortem). Comes with a red / yellow / green traffic light and concrete next actions.

Compared to similar skills

Drafted via tranfu-publish, signed off by the author. Helps readers pick horizontally between similar skills.

Inside the company library

• elite-market-researcher — deep sector research through a composite-analyst persona; difference: ai-startup-feasibility-check focuses on structural death-risk screening for AI startups (prohibition list + three-axis scoring), does no panoramic market research, and is designed to pair with the researcher rather than replace it
• business-analysis-pipeline — 7-step pipeline yielding a 120-point business feasibility report; difference: this skill zeroes in on AI-specific platform risks (foundation-model substitution / leverage direction / single-API dependency), whereas business-analysis-pipeline handles generic business analysis
• market-analysis — 12-dimension parallel search producing panoramic market analysis; difference: market-analysis owns the search and report structure, this skill turns those findings into an AI-startup feasibility verdict

Outside the company library

• /validate (e2larsen) — validates startup ideas by searching Reddit / App Store / Google Trends; difference: this skill does no data-driven search validation, instead applying the AI startup prohibition list as a structural death-risk screen

What makes this skill unique

• 13-item AI startup prohibition list (tiered 1/2/3) plus a three-axis scoring system
• Six built-in reference cases (negative: UUMit / Jasper / You.com, positive: Cursor / Mercor / LangSmith)
• Forms the AI-sector research trio together with elite-market-researcher / market-analysis

Working with it

Drafted with tranfu-publish guidance. Helps readers ramp up vertically.

What to prepare

• A concrete description of the AI project / direction under review (the more specific the better: what it does, who buys it, how it charges)
• Install elite-market-researcher and market-analysis alongside if you plan to combine them
• Best results come from running market-analysis first for search, then this skill for the feasibility verdict

Recommended use

• Feed in a concrete project description (e.g. "AI agents for SMB customer support"), not just a category label
• The two modules to focus on are "prohibition-list hits" and "three-axis scoring"
• Cross-check against the case library under references/ for the closest negative / positive analog

Known limits

• The prohibition list is scoped to AI startup directions and does not apply to non-AI projects
• No real-time data search; the skill relies on inputs from the user or on results piped in from other skills
• The case library is anchored in 2024-2026 examples and needs periodic refresh