Welcome to my AI GENERATED list of interesting repositories in the fields of Generative AI, Artificial Intelligence, and Data Science. As an AI Engineer & Product Development Specialist in these areas, I've compiled this list to help developers, researchers, and enthusiasts stay up-to-date with the latest advancements and tools.

This GitHub landing page is automatically updated every day by AI to showcase my latest interests and discoveries in the fields of AI, Machine Learning, and Data Science.

Here's how it works:

1. Automated Updates: The repository list you see below is automatically updated daily using GitHub Actions that I've set up to run the AI pipeline.
2. Starred Repositories: The content is based on my GitHub starred repositories, reflecting my current interests and valuable finds in the tech world.
3. AI-Powered Organization: An AI model (GPT-4o) is used to categorize and organize the repositories, ensuring that the list remains well-structured and informative.
4. Minimal Manual Intervention: Once set up, this page requires little to no manual updating. All I need to do is star a new repo, and it will automatically be categorized and added to this document. When I remove a repo from my starred list, the AI automatically removes it from this page.

Why use AI for this?: The key thing that the LLM-based solution is doing is dynamically deciding what categories should exist on this page and how to group my starred repos into those categories. It also reassesses and changes the categories and groupings over time as the starred repos change. It does all of this without the need for me to decide in advance what categories it should consider or to pre-train the model on categories. Implementing this algorithm is extremely simple compared to implementing a solution with a more traditional ML/AI approach that doesn't have the NLP reasoning capabilities of an LLM.

• Artificial Intelligence & AI Agents
• Developer Tools
• MLOps & AI Infrastructure
• Other
• How to Implement This Landing Page On Your Own GitHub
• 🌟 Contributing
• 📄 License

• agentscope-ai/QwenPaw - Your Personal AI Assistant; easy to install, deploy on your own machine or on the cloud; supports multiple chat apps with easily extensible capabilities.
• Panniantong/Agent-Reach - Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
• google/skills - Agent Skills for Google products and technologies.
• mattpocock/skills - Skills for Real Engineers. Straight from my .agents directory.
• obra/superpowers - An agentic skills framework & software development methodology that works.
• addyosmani/agent-skills - Production-grade engineering skills for AI coding agents.
• google/agents-cli - The CLI and skills that turn any coding assistant into an expert at creating, evaluating, and deploying AI agents on Google Cloud.
• livekit/agents - A framework for building realtime voice AI agents 🤖🎙️📹.
• NousResearch/hermes-agent - The agent that grows with you.
• multica-ai/andrej-karpathy-skills - A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
• rohitg00/agentmemory - Persistent memory for AI coding agents based on real-world benchmarks.
• bytedance/deer-flow - An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skills, subagents, and message gateway, it handles different levels of tasks that could take minutes to hours.
• SylphAI-Inc/atskills - The @skills protocol: use any SKILL.md by path, save it into .atskills/, auto-trigger it with one line. Reference implementation + integration guide for any agent.

• Nutlope/hallmark - Anti-AI-slop design skill for Claude Code, Cursor, and Codex.
• colbymchenry/codegraph - Pre-indexed code knowledge graph, auto syncs on code changes, for Claude Code, Codex, Gemini, Cursor, OpenCode, AntiGravity, Kiro, CoPilot, and Hermes Agent — fewer tokens, fewer tool calls, 100% local.
• github/spec-kit - 💫 Toolkit to help you get started with Spec-Driven Development.
• abhigyanpatwari/GitNexus - GitNexus: The Zero-Server Code Intelligence Engine. GitNexus is a client-side knowledge graph creator that runs entirely in your browser. Drop in a git repository (Github, Gitlab, Azure, Local) or ZIP file, and get an interactive knowledge graph with a built-in Graph RAG Agent. Perfect for code exploration.
• entireio/cli - 📜 Entire CLI hooks into your Git workflow to capture AI agent sessions as you work. Sessions are indexed alongside commits, creating a searchable record of how code was written in your repo.
• openclaw/mcporter - Call MCPs via TypeScript, masquerading as simple TypeScript API. Or package them as CLI.
• excalidraw/excalidraw-mcp - Fast and streamable Excalidraw MCP App.

• meituan-longcat/LongCat-Video - None
• deepseek-ai/deepseek-harness - DeepSeek Harness: Everything is a Plugin.

• andrewyng/openworker - OpenWorker project repository.
• PriorLabs/TabPFN - ⚡ TabPFN: Foundation Model for Tabular Data ⚡.
• google-research/tabfm - TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification.
• koala73/worldmonitor - Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface.
• block/buzz - A hive mind communication platform.
• kaishi00/hermes-conduit - Conduit — native SwiftUI iOS client for Hermes Agent.
• anthropics/claude-for-legal - A suite of plugins for legal workflows.
• D4Vinci/Scrapling - 🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!

This project automatically updates the README with your GitHub starred repositories using a Python script and GitHub Actions.

1. Clone the repository:

   git clone https://github.com/your-username/github-genai-list.git  
   cd github-genai-list
   

2. Set up your environment: Ensure you have Python 3.x installed. You can check your version with:

   python --version
   

3. Install dependencies: First, make sure pip is up-to-date:

   python -m pip install --upgrade pip
   

   Then install the required dependencies:

   pip install -r requirements.txt
   

4. Set up GitHub secrets: Go to your GitHub repository settings, and under Secrets and Variables > Actions, create the following secrets:

   • GITHUB_TOKEN: Your personal access token for GitHub API.
   • OPENAI_API_KEY: Your API key for OpenAI (if applicable for LLM updates).
   • PAT: A GitHub Personal Access Token with appropriate permissions.

5. Update the script: In the update_readme.py script located in the scripts/ directory, replace "YourGitHubUsername" with your actual GitHub username.

6. Configure the GitHub Action: The workflow is already set up in .github/workflows/update-readme.yml. It runs daily at midnight (UTC) or can be triggered manually via the GitHub Actions tab.

7. Running locally (optional): You can also run the script manually to test it locally:

   python scripts/update_readme.py
   

8. Commit and push changes:

   git add .
   git commit -m "Updated project structure"
   git push origin main
   

9. Customize the README: You can modify the README.md template to fit your personal brand and interests. The GitHub Action will automatically update it with your latest starred repositories daily.

Note: This landing page uses OpenAI's GPT-4o model. Make sure you comply with OpenAI's use-case policies and monitor your API usage to manage costs.

Feel free to open a pull request if you have any suggestions for additions or improvements to this list. Let's collaborate to keep this resource up-to-date and valuable for the community!

This project is licensed under the MIT License - see the LICENSE.md file for details.

📊 Last edited: 2026-08-31

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