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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)

# [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