| name | clone-discovery |
| description | Discovery phase -- compare 20+ competing services, build score matrix, select best clone target |
Clone Discovery
When to Use
- Starting a clone project from scratch -- need to pick the right target
- Have a product category but no specific product chosen
- Need structured comparison of GenAI market segment
Process
- Define category -- narrow down the product space (e.g. "AI presentation builders", "AI writing assistants")
- Web search -- find 20+ products via Product Hunt, G2, AlternativeTo, GitHub Awesome lists, Reddit threads
- Build comparison table -- for each product capture:
- Name, URL, pricing (free tier / paid / enterprise)
- Core features (bullet list)
- Tech stack (frontend, backend, AI model)
- Open-source availability (full OSS / partial / closed)
- User base & traction (downloads, stars, reviews)
- Score matrix -- rate each product 1-5 on:
- Openness -- how much code/architecture is visible
- Complexity -- effort to replicate (lower = better for cloning)
- Market fit -- demand and willingness to pay
- Differentiation potential -- room to improve over original
- Select target -- pick the product with highest composite score
- Write rationale -- 3-5 sentences explaining why this target was chosen
Output
- Comparison table (markdown) with 20+ entries
- Score matrix with weighted scores
- Recommended target + rationale document
- List of runner-up alternatives
Example
Category: "AI slide generators". Search yields Gamma.app, Beautiful.ai, Tome, SlidesAI, Decktopus, etc. Score matrix shows Gamma.app wins on market fit (4.8M users) + moderate complexity + partial API visibility. Recommendation: clone Gamma.app with focus on open-source LLM backend.
