Loading...
Loading...
Found 245 Skills
Build backend AI with Vercel AI SDK v6 stable. Covers Output API (replaces generateObject/streamObject), speech synthesis, transcription, embeddings, MCP tools with security guidance. Includes v4→v5 migration and 15 error solutions with workarounds. Use when: implementing AI SDK v5/v6, migrating versions, troubleshooting AI_APICallError, Workers startup issues, Output API errors, Gemini caching issues, Anthropic tool errors, MCP tools, or stream resumption failures.
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
Install and configure Ollama for local embeddings with GrepAI. Use this skill when setting up private, local embedding generation.
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimizationUse when "rag, retrieval augmented, vector search, embeddings, semantic search, document qa, rag, retrieval, embeddings, vector, search, llm" mentioned.
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).
OpenAI API via curl. Use this skill for GPT chat completions, DALL-E image generation, Whisper audio transcription, embeddings, and text-to-speech.
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
Configure pgvector extension for vector search in Supabase - includes embedding storage, HNSW/IVFFlat indexes, hybrid search setup, and AI-optimized query patterns. Use when setting up vector search, building RAG systems, configuring semantic search, creating embedding storage, or when user mentions pgvector, vector database, embeddings, semantic search, or hybrid search.
Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Claude, Gemini, Llama), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Python SDK examples.
AST-based semantic code search skill for AI agents. Teaches agents to use sqry's 34 MCP tools for finding symbols by structure (functions, classes, types), tracing relationships (callers, callees, imports, inheritance), analyzing dependencies, and detecting code quality issues. Unlike embedding-based search, sqry parses code like a compiler. Supports 37 languages. Uses tiered discovery: start with Quick Tool Selection below, load reference files only when you need parameter details or advanced workflows.
Full-stack hybrid memory system with vector + keyword search. Stores embeddings in SQLite with FTS5 for BM25 keyword search and cosine similarity. Enables semantic memory recall for agents.