gemini-agent-sdk-setup
Integrate OpenAI Agent SDK with Google Gemini backend into FastAPI service using the official Google Gemini API via OpenAI compatibility layer.
Browse published AI skills maintained by nadeemsangrasi. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
15Repository stars across indexed skills
0Repository forks
0Average repository stars per indexed skill
0Repository stars may repeat across skills from the same repository.
Integrate OpenAI Agent SDK with Google Gemini backend into FastAPI service using the official Google Gemini API via OpenAI compatibility layer.
Integrates Better Auth client with ChatKit UI or custom Next/Vite UI. Adds login/logout buttons, protected routes, session hooks.
Add selection-based Q&A functionality to ChatKit UI allowing users to ask about highlighted text with proper integration.
Generate the full production Dockerfile for the backend with multi-stage build, proper dependencies, and Hugging Face Spaces compatibility.
Generates Drizzle ORM schema files for users, sessions, tokens, OAuth accounts, and audit logs. Syncs schema to Neon Postgres.
Generate the complete RAG ingestion script to crawl textbook from sitemap.xml, chunk content, embed using Gemini, and push to Qdrant following MCP documentation.
Generate Hugging Face Spaces configuration files including runtime setup, environment variables, and deployment instructions.
Generate and maintain the FastAPI backend structure for the RAG chatbot with proper project organization.
Modify OpenAI ChatKit starter to use FastAPI backend instead of OpenAI API, including citation support and error handling.
Generate JSON schemas and Python handlers for Qdrant-based retrieval tools used by the RAG agent to search sitemap-crawled textbook content with URL citations.
Creates Better Auth configuration for frontend and backend: handlers, providers, middleware, session/token options. Uses Context7 MCP to retrieve Better Auth docs.
Implement the /chat endpoint that performs agent-based RAG reasoning with proper input validation and response formatting.
Provide local debugging scripts for Docker including build, run, and environment setup for development workflow.
Implement reusable embedding functions using Gemini embedding models via LangChain with proper error handling and batching for sitemap-crawled content.
Customize ChatKit layout, theme, and presentation for RAG textbook chatbot with proper styling and textbook theme integration.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.