Imported from dlwlrmax/.dotfiles (opencode/.config/opencode/skills/data-ai-category-pointer/SKILL.md). Install upstream with npx skills add dlwlrmax/.dotfiles --skill data-ai-category-pointer. Copyright stays with the author.

Data Ai Capability Library 🎯

This is a pointer skill. The 14 specialized Data Ai skills are stored in a hidden vault to keep your startup context minimal.

Available skills in this category

• ai-engineering-toolkit — 6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

• clarity-gate — Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.

• embedding-strategies — Guide to selecting and optimizing embedding models for vector search applications.

• exa-search — Semantic search, similar content discovery, and structured research using Exa API. Use when you need semantic/embeddings-based search, finding similar content, or searching by category (company, people, research papers, etc.).

• llm-app-patterns — Production-ready patterns for building LLM applications, inspired by Dify and industry best practices.

• local-llm-expert — Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.

• notebooklm — Interact with Google NotebookLM to query documentation with Gemini's source-grounded answers. Each question opens a fresh browser session, retrieves the answer exclusively from your uploaded documents, and closes.

• rag-engineer — Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

• recsys-pipeline-architect — Designs composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework

• seek-and-analyze-video — Seek and analyze video content using Memories.ai Large Visual Memory Model for persistent video intelligence

• similarity-search-patterns — Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

• tavily-web — Web search, content extraction, crawling, and research capabilities using Tavily API. Use when you need to search the web for current information, extracting content from URLs, or crawling websites.

• vector-database-engineer — Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

• vector-index-tuning — Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

How to load a skill

1. Identify the skill name above matching your task.
2. Use view_file to read its SKILL.md from the vault: /home/kienct/.config/opencode/skill-libraries/data-ai/<skill-name>/SKILL.md
3. Follow those instructions to complete the request.

Vault path: /home/kienct/.config/opencode/skill-libraries/data-ai

Do not guess best practices — always read from the vault first.