AI-powered system for Creating & Executing your own Claude Skills 🤹 with GitHub

This workflow implements an advanced AI-powered system for generating, and executing Claude Skills stored on GitHub. When creating a skill, the workflow: Uses an AI agent to generate a properly structured SKILL.md file Extracts and formats the skill content Saves it automatically into a GitHub repository under a structured /skills directory When executing a skill, the workflow: Dynamically lists available skills from GitHub Navigates directories to find the correct skill files Retrieves their content via API Executes instructions strictly based on those files using an AI agent --- Key Benefits 1. ✅ Dynamic Skill Execution The system doesn’t rely on hardcoded logic. It retrieves and executes skills directly from GitHub, making it highly flexible and extensible. 2. ✅ Self-Extending Architecture New capabilities can be added simply by creating new skills. The workflow automatically integrates them without requiring changes to the core system. 3. ✅Separation of Logic and Execution All instructions are stored in external skill files, keeping the workflow clean, modular, and easy to maintain. 4. ✅ Automated Skill Creation The workflow can generate complete Claude Skills (including structured documentation) and publish them to GitHub without manual intervention. 5. ✅ Multi-Model Intelligence By combining OpenAI and Anthropic models, the system leverages different strengths (reasoning, generation, structure). 6. ✅ Context-Aware Conversations Memory nodes allow the system to maintain session context, improving continuity and personalization. 7. ✅ Reliable Output Handling Structured output parsing ensures decisions (like whether to proceed or ask for more info) are deterministic and machine-readable. 8. ✅ Up-to-Date Knowledge via Context7 Before generating skills, the system fetches real documentation, reducing hallucinations and ensuring accuracy. 9. ✅ GitHub as a Skill Registry Using GitHub as a storage layer provides: Version control Collaboration Transparency Easy scaling 10. ✅ Agent-Based Orchestration The workflow uses multiple specialized AI agents, each with a clear responsibility (validation, generation, execution), improving robustness and clarity. --- How it works 1. Creating new skills interactively with the help of a dedicated agent that uses Context7 for up‑to‑date documentation. 2. Executing existing Claude skills stored in a GitHub repository (Claude Skills + Github System) Creation flow (making a new skill) 1. The AI Conversational Agent decides if the user is trying to create a skill and whether enough information is available. 2. If information is missing, the user is asked for clarification via the More info node. 3. Once ready, the Claude Skills Creator Agent takes over. 4. This agent always consults Context7 to fetch documentation for any libraries/APIs involved. 5. It then generates a properly formatted SKILL.md file (with YAML frontmatter, imperative style, ≤500 lines). 6. The Extract Skill MD node parses the generated output. 7. The SKILL.md Parser converts the markdown into binary data. 8. The Create a Skill node uploads the file to the correct path in the GitHub repository (skills/<skill‑name>/SKILL.md). 9. Finally, the Skill created node confirms success in the chat. Execution flow (using skills) 1. A chat message arrives → the workflow checks if the user wants to create a skill (via the AI Conversational Agent). 2. If the request is not about creating a skill, it proceeds to the Skills Agent. 3. The Skills Agent first receives a list of all skill directories from GitHub (via the List Skills node). 4. It then uses two GitHub tools to explore those directories: - List Files – to browse the contents of a skill folder. - Get File from Skill – to fetch the actual SKILL.md or other resources. 5. The agent follows the instructions found in the skill files (not its own general knowledge) and produces an answer. 6. The final answer is sent back to the chat via the Respond node. 7. A Simple Memory node maintains