Painpoint Discovery Expert

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".

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:

text

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