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", "a...

Security Scan

The skill's requests and runtime instructions are consistent with a web-research-focused painpoint discovery assistant — nothing it asks for or tells the agent to do is disproportionate to that purpose.

Purpose & Capability

Name/description, declared browser tool requirement, and instructions (web searches, forum scraping, report generation) align with a web-research painpoint discovery workflow. No unrelated credentials, binaries, or config paths are requested.

Instruction Scope

Instructions direct the agent to run searches, open result pages, snapshot content, extract quotes and cluster complaints — all expected for this purpose. Note: the skill explicitly recommends scraping social media/forums (Reddit/Quora) and quoting source comments; that may collect public PII and could raise site terms-of-service or privacy concerns. Also deep-research mode spawns longer sessions and will save report files.

Install Mechanism

Instruction-only skill with no install spec and no code files — lowest install risk (nothing is downloaded or written by an installer).

Credentials

Requires no environment variables, credentials, or config paths. The lack of secret requests is proportionate to the claimed functionality.

Persistence & Privilege

always:false and default autonomous invocation is used. The skill documents spawning of a subagent for deep research (sessions_spawn) that can run longer and produce files; this is consistent with its purpose but means a deeper-running, semi-autonomous task will execute if the user requests it — consider platform limits and oversight for long-running subagents.

Assessment

This skill appears to do what it says: web-search and scrape public content to build painpoint reports. Before enabling or running it, consider: 1) Browser scraping: it will access public pages and may capture user comments that contain personal data — review and redact PII before sharing externally. 2) Terms of service: automated scraping of sites like Reddit/Quora may violate their terms; prefer using official APIs or rate-limited, polite scraping. 3) Subagent runs: deep-research spawns longer subagents that can run for many minutes and save report files — ensure you’re comfortable with that autonomy and any platform quotas. 4) Health/legal content: when exploring health or regulated domains, add disclaimers and avoid providing medical advice. 5) Validation: outputs are research-driven but not validated; follow up with interviews or experiments before making product decisions. If any of the above are unacceptable, restrict the skill to quick mode only or require user approval before spawning subagents or scraping specific domains.

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