Skill: lyuxing/painpoint-discovery (version 1.0.1)

--- name: painpoint-discovery description: Painpoint discovery expert. Helps users discover, analyze, and evaluate startup painpoints in specific domains. Use when user says "find painpoints in X", "analyze opportunities in X", "startup research". metadata: { "openclaw": { "requires": { "bins": [], "tools": ["browser"] }, "install": [], }, } --- # Painpoint Discovery Expert ## Use Cases Use when users want to explore startup opportunities in a specific domain: - "Find painpoints in the weight loss domain" - "Analyze opportunities in remote work" - "I want to build something in pet care, what painpoints are worth solving" ## Core Capabilities ### 1. Web Research & Mining - Search social media, forums, reviews for user complaints - Analyze discussion volume around related topics - Identify high-frequency problems and frustrations ### 2. Painpoint Structuring Each painpoint includes: - **Scenario**: When/where does this problem occur - **Problem**: What specifically is the frustration - **Target Audience**: Who experiences this painpoint - **Frequency**: How often does it occur - **Existing Solutions**: Current approaches and why they fall short ### 3. Solution Recommendations Based on painpoint type, recommend: - 📱 **App/Mini-program**: High-frequency, lightweight, mobile scenarios - 💻 **SaaS/Tool**: Workflow, B2B, requires ongoing use - 📦 **Hardware**: Physical interaction, sensors, dedicated devices - 📚 **Content/Education**: Knowledge gaps, skill learning - 🤝 **Service/Platform**: Connecting supply/demand, matching problems ### 4. Business Value Assessment - **Market Size**: Potential users × willingness to pay - **Competition**: Number and quality of existing players - **Entry Barrier**: Technical/capital/resource requirements - **Willingness to Pay**: Will users actually spend money - **Recommendation**: ⭐⭐⭐⭐⭐ (1-5 stars) ## Output Formats ### Mode 1: Comprehensive Report (Default) ```markdown # [Domain] Painpoint Analysis Report ## Research Sources - Search keywords: [...] - Data sources: [social media/forums/reviews/news] - Research date: [date] ## Painpoint 1: [Name] [...] ## Painpoint Knowledge Graph [...] ## Next Steps & Recommendations [...] ``` ### Mode 2: AhaPoints Format (`--ahapoints`) Generate independent AhaPoint reports for each painpoint: ``` ahapoints-protocol/points/ ├── YYYYMMDD-HHMM-PAIN-[Title1].md ├── YYYYMMDD-HHMM-PAIN-[Title2].md └── YYYYMMDD-HHMM-INNO-[Title3].md ``` Uses AhaPoints Protocol v1.0 template: 1. Point Type 2. One-liner Description 3. Scenario Story 4. Why It Matters 5. Potential Solution Directions 6. Validation Methods 7. Metadata **Priority**: Timestamp on each report serves as priority proof ## Workflow (Browser Mode - No API Required) 1. **Confirm Domain**: Ask user which domain/topic to explore 2. **Open Search Engine**: - `browser.navigate("https://www.google.com/search?q=[domain]+problems+OR+frustration+OR+complaints")` 3. **Capture Search Results**: - `browser.snapshot()` → Extract titles and summaries - Record high-quality result links 4. **Deep Scraping** (Optional): - `browser.navigate(link)` Open specific pages - `browser.snapshot()` → Extract detailed content 5. **Structured Output**: Compile into report format 6. **Optional Deep Dive**: User can select a painpoint for further research ## Workflow (API Mode - If Brave Configured) 1. **Confirm Domain** 2. **web_search** for complaints and discussions 3. **web_fetch** specific pages for content analysis 4. **Structured Output** ## Important Notes - Prioritize **specific complaints**, not vague "I want X" - Distinguish **real painpoints** (willing to pay) from **pseudo-needs** (just talking) - Remind users: research results need real-world validation - Encourage users to supplement with their own experiences - **Browser mode is slower** - recommend 3-5 search keywords max


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)