Painpoint Discovery Expert

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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": [], }, }

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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