🤖 AI Skill Insight Engine

An AI-powered tool to analyze and understand AI skills using Pollinations AI.

This application automatically analyzes AI skill documentation and provides structured insights including summaries, key functionalities, potential use cases, and improvement suggestions.

Powered by Pollinations AI - The world's largest generative AI network.

✨ Features

• Automated Analysis: Fetch and analyze AI skill documentation from any URL
• AI-Powered Insights: Uses Pollinations AI to extract meaningful information
• Multiple Output Formats: Generate reports in Markdown, JSON, or HTML
• Easy Integration: Simple CLI interface and modular architecture
• Pollinations AI Integration: Leverages advanced language models for analysis

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• Pollinations AI API key (get yours at https://enter.pollinations.ai)

Installation

1. Clone the repository:

   git clone https://github.com/sobatanonimak/ai_skill_insight_engine.git
   cd ai_skill_insight_engine

2. Install dependencies:

   pip install -r requirements.txt

3. Configure API key:

   cp .env.example .env
   # Edit .env and add your Pollinations API key

Usage

Basic usage:

python app.py https://example.com/skill.md

Save report to file:

python app.py https://example.com/skill.md --output report.md

Generate JSON output:

python app.py https://example.com/skill.md --output report.json --format json

Generate HTML report:

python app.py https://example.com/skill.md --output report.html --format html

📖 How It Works

The AI Skill Insight Engine follows a 4-step pipeline:

1. Fetch: Retrieves content from the provided URL (supports HTML, Markdown, plain text)
2. Parse: Cleans and extracts readable text from the content
3. Analyze: Sends the parsed text to Pollinations AI for analysis
4. Generate: Formats the analysis results into your preferred output format

Architecture

┌─────────────┐     ┌─────────────┐     ┌──────────────┐     ┌──────────────┐
│  fetcher.py │ ──> │  parser.py  │ ──> │ analyzer.py  │ ──> │ generator.py │
│  (Fetch)    │     │  (Parse)    │     │ (Analyze)    │     │ (Generate)   │
└─────────────┘     └─────────────┘     └──────────────┘     └──────────────┘
       │                   │                    │                    │
       └───────────────────┴────────────────────┴────────────────────┘
                                    │
                              ┌─────────────┐
                              │   app.py    │
                              │  (Orchestrate)
                              └─────────────┘

🔧 Configuration

Environment Variables

Create a .env file in the project root:

# Required: Your Pollinations AI API key
POLLINATION_API_KEY=your_api_key_here

# Optional: Model to use for analysis
# Available models: pollinations/gemini-search, pollinations/kimi, pollinations/deepseek, etc.
POLLINATION_MODEL=pollinations/gemini-search

# Optional: Frontend API Key for authentication
# Set this to restrict API access to your frontend only
# Generate a secure random string (e.g., using: openssl rand -hex 32)
# FRONTEND_API_KEY=your_secure_random_key_here

🔐 Security: Frontend API Key

To restrict API access so only your frontend can use it:

1. Generate a secure API key:

   openssl rand -hex 32

2. Set in backend (Vercel Environment Variables):

   • Variable name: FRONTEND_API_KEY
   • Value: The generated key from step 1

3. Set in frontend (Vercel Environment Variables):

   • Variable name: NEXT_PUBLIC_API_KEY
   • Value: The same key from step 1

When enabled, the backend will require a valid X-API-Key header on all /analyze requests. Requests without a valid key will receive a 401 Unauthorized error.

Note: The /health endpoint remains open for monitoring purposes.

Available Models

The engine supports various Pollinations AI models:

• pollinations/gemini-search - Google's multimodal model with search (recommended)
• pollinations/kimi - Moonshot's Kimi model
• pollinations/deepseek - DeepSeek model
• pollinations/glm - GLM model
• pollinations/claude-haiku - Anthropic's Claude Haiku
• pollinations/openai - OpenAI models

📊 Example Output

Markdown Report

# AI Skill Analysis Report

**Generated:** 2026-03-23 06:00:00 UTC
**Source:** https://example.com/skill.md
**Model Used:** pollinations/gemini-search

---

## 📋 Summary
This skill provides weather data and forecasts for multiple locations...

---

## ⚙️ Key Functionalities
1. Fetch current weather data
2. Provide hourly and daily forecasts
3. Support multiple locations worldwide

---

## 💡 Potential Use Cases
1. Personal weather tracking
2. Travel planning
3. Event planning based on weather conditions

---

## 🚀 Improvement Suggestions
1. Add severe weather alerts
2. Integrate with calendar applications
3. Support for historical weather data

🧪 Testing

Run the test suite:

# Run individual module tests
python fetcher.py
python parser.py
python analyzer.py
python generator.py

# Run full application test (requires API key)
python app.py https://www.moltbook.com/skill.md --output test_report.md

🌐 Deployment (Vercel)

This application can be deployed on Vercel as a serverless function:

1. Push to GitHub:

   git init
   git add .
   git commit -m "Initial commit: AI Skill Insight Engine"
   git remote add origin https://github.com/sobatanonimak/ai_skill_insight_engine.git
   git push -u origin main

2. Deploy to Vercel:

   • Go to https://vercel.com
   • Import your GitHub repository
   • Configure environment variables (POLLINATION_API_KEY)
   • Deploy!

3. Set up API endpoint (optional):

   • Create api/analyze.py for serverless function
   • Configure Vercel routing

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is part of the Pollinations AI ecosystem and is built to showcase the capabilities of Pollinations AI models.

🙏 Acknowledgments

• Pollinations AI - For providing the powerful API and models that power this analysis engine
• OpenClaw - For the development environment and tools
• Moltbook - For the AI agent community

🎨 Brand Assets

This project proudly uses official Pollinations AI branding:

• Badge: Built With pollinations.ai
• Logo White: Download
• Logo Text White: Download
• Brand Guidelines: Pollinations AI Brand

📞 Support

For issues, questions, or suggestions:

• Open an issue on GitHub
• Discuss on Moltbook (AI agent community)
• Check Pollinations AI documentation: https://pollinations.ai
• Join Pollinations Discord: https://discord.gg/pollinations

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Built with ❤️ using Pollinations AI

AI Skill Insight Engine - Helping you understand AI skills better