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Find and validate startup ideas by mining user complaints, crafting premises, and navigating the idea maze. Use when discovering product opportunities, validating ideas, shaping solutions, researching user pain points, or exploring what to build.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | validating-startup-ideas |
| description | Find and validate startup ideas by mining user complaints, crafting premises, and navigating the idea maze. Use when discovering product opportunities, validating ideas, shaping solutions, researching user pain points, or exploring what to build. |
| metadata | {"author":"nweii","version":"1.6.0"} |
Apply these frameworks as lenses to what the user brings—use them to surface blindspots, ask sharper questions, or reframe what's being worked on. This skill works at any stage of idea maturity.
Every complaint signals someone willing to pay for a better solution. But finding complaints is only the start—great products give users a premise that normalizes new behavior.
Mine complaints from platforms where users express frustration:
| Product type | Source | Search for |
|---|---|---|
| B2B | G2, Capterra (1-2★ reviews) | "doesn't have", "wish it could", "missing", "can't" |
| B2C services | Reddit + "[topic] frustrating" | r/mildlyinfuriating, niche hobby subs, r/entrepreneur |
| Automation | Upwork job posts | "weekly", "monthly", "ongoing", "repeat" |
| Mobile apps | App Store 1★ reviews | Same complaint 20+ times |
Reddit: Sort by comments, not upvotes. High comments = heated debate = real problem.
Upwork: If 100+ people paying $20/hr for a task, they'll pay $50/mo to automate it.
From Lenny Rachitsky's B2B framework—four signs your idea has legs:
Quick validation checklist:
Dalton Caldwell (YC): Tarpits are ideas that seem promising, validate easily, and attract lots of founders—but systematically fail. Classic examples: "apps to coordinate hangouts with friends," music discovery, Foursquare clones. Warning signs:
Bangaly Kaba (Instagram, Instacart): Consider who's just outside your current user base. The "adjacent user" is someone who could use your product but doesn't—often because of a single friction point. When you solve for them, you unlock the next growth wave.
Don't build what users ask for. Solve the underlying problem better.
Bad: "Notion is too complex" → simpler Notion clone
Good: "Notion is too complex" → focused tool for one specific use case
Great products give users a premise—a foundational belief that normalizes otherwise-atypical actions:
The best premises become core features. Bumble required women to message first—the premise became the product mechanic.
Ivan Zhao (Notion): If your vision is "broccoli" (something users don't naturally seek out), wrap it in "sugar" (a form factor they already use daily). Notion wanted to build "Lego for software"—but nobody wakes up wanting that. So they hid it inside productivity software, a category people already cared about. The vision is still there; it's just discovered gradually.
Mike Maples Jr (Floodgate): Don't be a "10x better Apple." Be the world's first banana. Great ideas make people choose you or not—they can't compare you to the status quo. If customers can substitute something else for your solution, they won't take the risk on a startup.
For deeper guidance on crafting premises and shaping ideas strategically, see references/shaping-ideas.md.
Fons Mans approach:
From Paul Graham: Real startups discover the problem through evolution. Operate cheaply. Let ideas evolve. The market doesn't care how hard you worked—only whether you built what users want.
OpenClaw Gateway setup, configuration, and best practices. Use when installing OpenClaw, configuring channels (WhatsApp/Telegram/Discord), setting up providers (OpenAI/Google/Anthropic), creating skills, managing the gateway daemon, or troubleshooting OpenClaw issues. Triggers on: openclaw, gateway, whatsapp channel, telegram bot, openclaw skill, openclaw config, openclaw install, openclaw cron, openclaw security.
Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains.
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning. **This project:** We use OpenAI text-embedding-3-small (1536) and store as vector(1536) in knowledge_chunks. halfvec is an optional future optimization; apply this skill's tuning (ef_search, iterative_scan
Supabase pgvector setup, RAG pipeline, and vector search for StartupAI. Covers knowledge_chunks schema, HNSW/IVFFlat indexes, search_knowledge + hybrid_search_knowledge RPCs, embedding generation via OpenAI, Edge Function integration, and Gemini web search grounding. **Trigger when user asks to:** - Set up or modify vector search, embeddings, or knowledge base - Ingest documents into knowledge_chunks - Debug search quality or missing results - Tune HNSW parameters or search performance - Wire RAG into edge functions or AI chat - Use Google Search grounding or URL Context with Gemini **This project:** OpenAI text-embedding-3-small (1536 dims), stored in knowledge_chunks, HNSW index, search via search_knowledge() and hybrid_search_knowledge() RPCs.
pgvector - PostgreSQL extension for vector similarity search. Use for embedding storage, cosine similarity, IVFFlat indexes, and HNSW indexes.
Channel the strategic thinking of fastest-growing AI startup founders. Use when asked to analyze current state, brainstorm strategy, set OKRs, or create execution plans. Provides founder personas, strategic frameworks, and battle-tested patterns from Anthropic, OpenAI, Mistral, Scale AI, and others.
