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Found 226 Skills
Diagnose context stuffing vs. context engineering. Assess practices, define boundaries, and advise on memory architecture, retrieval, and the Research→Plan→Reset→Implement cycle.
Build chat interfaces for querying documents using natural language. Extract information from PDFs, GitHub repositories, emails, and other sources. Use when creating interactive document Q&A systems, knowledge base chatbots, email search interfaces, or document exploration tools.
Use this skill when building NLP pipelines, implementing text classification, semantic search, embeddings, or summarization. Triggers on text preprocessing, tokenization, embeddings, vector search, named entity recognition, sentiment analysis, text classification, summarization, and any task requiring natural language processing.
Use this skill when building production LLM applications, implementing guardrails, evaluating model outputs, or deciding between prompting and fine-tuning. Triggers on LLM app architecture, AI guardrails, output evaluation, model selection, embedding pipelines, vector databases, fine-tuning, function calling, tool use, and any task requiring production AI application design.
Use when working with code refactoring context restore
Claude AI cookbooks - code examples, tutorials, and best practices for using Claude API. Use when learning Claude API integration, building Claude-powered applications, or exploring Claude capabilities.
Upstash Vector DB setup, semantic search, namespaces, and embedding models (MixBread preferred). Use when building vector search features on Vercel.
Design and implement memory architectures for agent systems. Use when building agents that need to persist state across sessions, maintain entity consistency, or reason over structured knowledge.
Perform autonomous, multi-step research using the Gemini Deep Research Agent (Interactions API). Supports web search, file/directory context, and resilient streaming.
PocketFlow framework for building LLM applications with graph-based abstractions, design patterns, and agentic coding workflows
AI/ML APIs, LLM integration, and intelligent application patterns
PyTiDB (pytidb) setup and usage for TiDB from Python. Covers connecting, table modeling (TableModel), CRUD, raw SQL, transactions, vector/full-text/hybrid search, auto-embedding, custom embedding functions, and reference templates/snippets (vector/hybrid/image) plus agent-oriented examples (RAG/memory/text2sql).