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Found 1,637 Skills
Complete guide for OpenAI's Assistants API v2: stateful conversational AI with built-in tools (Code Interpreter, File Search, Function Calling), vector stores for RAG (up to 10,000 files), thread/run lifecycle management, and streaming patterns. Both Node.js SDK and fetch approaches. ⚠️ DEPRECATION NOTICE: OpenAI plans to sunset Assistants API in H1 2026 in favor of Responses API. This skill remains valuable for existing apps and migration planning. Use when: building stateful chatbots with OpenAI, implementing RAG with vector stores, executing Python code with Code Interpreter, using file search for document Q&A, managing conversation threads, streaming assistant responses, or encountering errors like "thread already has active run", vector store indexing delays, run polling timeouts, or file upload issues. Keywords: openai assistants, assistants api, openai threads, openai runs, code interpreter assistant, file search openai, vector store openai, openai rag, assistant streaming, thread persistence, stateful chatbot, thread already has active run, run status polling, vector store error
Multi-directory context patterns for monorepos. Use when working with --add-dir, per-service CLAUDE.md, or separating root vs service context
Initialize and manage specification directories with auto-incrementing IDs. Use when creating new specs, checking spec status, tracking user decisions, or managing the docs/specs/ directory structure. Maintains README.md in each spec to record decisions (e.g., PRD skipped), context, and progress. Orchestrates the specification workflow across PRD, SDD, and PLAN phases.
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
Zustand state management best practices for React applications. Use when writing, reviewing, or refactoring Zustand stores to ensure optimal performance and maintainability. Triggers on tasks involving state management, stores, selectors, re-renders, and Zustand patterns.
Maps the directory structure of the project to help the AI understand the codebase layout.
Use when implementing agent memory, persisting state across sessions, building knowledge graphs, tracking entities, or asking about "agent memory", "knowledge graph", "entity memory", "vector stores", "temporal knowledge", "cross-session persistence"
Guidance for data resharding tasks that involve reorganizing files across directory structures with constraints on file sizes and directory contents. This skill applies when redistributing datasets, splitting large files, or reorganizing data into shards while maintaining constraints like maximum files per directory or maximum file sizes. Use when tasks involve resharding, data partitioning, or directory-constrained file reorganization.
RAG, embedding, vector search를 통해 사내/최신 데이터를 LLM 응답에 연결하는 방법과 선택 기준을 다루는 모듈.
Create, edit, and build Observable Notebooks using Notebook Kit. Use when working with .html notebook files, generating static sites from notebooks, querying databases from notebooks, or using data loaders (Node.js/Python/R) in notebooks. Covers notebook file format, cell types, CLI commands, database connectors, and JavaScript API.
Retrieval-Augmented Generation - chunking strategies, embedding, vector search, hybrid retrieval, reranking, query transformation. Use when building RAG pipelines, knowledge bases, or context-augmented applications.
Shipp is a real-time data connector. Use it to fetch authoritative, changing external data (e.g., sports schedules, live events) via the Shipp API.