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Found 1,574 Skills
Coverage analysis measures code exercised during fuzzing. Use when assessing harness effectiveness or identifying fuzzing blockers.
Complete file handling including upload flows, serving files via URL, storing generated files from actions, deletion, and accessing file metadata from system tables
Build with Firebase Cloud Storage - file uploads, downloads, and secure access. Use when: uploading images/files, generating download URLs, implementing file pickers, setting up storage security rules, or troubleshooting storage/unauthorized, cors errors, quota exceeded, or upload failed errors. Prevents 9 documented errors.
SQLite file format, B-trees, pages, cells, overflow, freelist that is used in tursodb
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
Complete guide for CloudBase cloud storage using Web SDK (@cloudbase/js-sdk) - upload, download, temporary URLs, file management, and best practices.
Configure GOB local file storage for GrepAI. Use this skill for simple, single-machine setups.
Configure Qdrant vector database for GrepAI. Use this skill for high-performance vector search.
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.
Coverage Gaps audit worker (L3). Identifies missing tests for critical paths (Money 20+, Security 20+, Data Integrity 15+, Core Flows 15+). Returns list of untested critical business logic with priority justification.
Build Retrieval-Augmented Generation (RAG) applications that combine LLM capabilities with external knowledge sources. Covers vector databases, embeddings, retrieval strategies, and response generation. Use when building document Q&A systems, knowledge base applications, enterprise search, or combining LLMs with custom data.
Complete RAG and search engineering skill. Covers chunking strategies, hybrid retrieval (BM25 + vector), cross-encoder reranking, query rewriting, ranking pipelines, nDCG/MRR evaluation, and production search systems. Modern patterns for retrieval-augmented generation and semantic search.