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Found 1,653 Skills
Build complete document knowledge bases with PDF text extraction, OCR for scanned documents, vector embeddings, and semantic search. Use this for creating searchable document libraries from folders of PDFs, technical standards, or any document collection.
Organize and synchronize session directory structure based on project architecture
When creating vector artwork, illustrations, or SVG graphics for creative expression - provides iterative drawing workflow with visual feedback using render-svg tool
Take focused, region-specific screenshots from web pages. Navigates to the right page based on user context (URL, search query, social media post), locates the target region via DOM selectors, and crops to a clean, focused screenshot.
Provision a dedicated PolarDB-X distributed database instance instantly with no auth required. Each instance is a full 2C4G standard edition with MySQL compatibility, distributed transactions, and vector search.
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.
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.
Knowledge Base RAG implements the complete Retrieval-Augmented Generation pipeline: document ingestion, intelligent chunking, embedding generation, vector store indexing, semantic retrieval, and grounded response generation.
Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, text truncation.
Create and manage a DDD bounded context with standard directory structure
创建包含 sections、problems、solutions 和 explainers 的 exercise directory structures，并确保通过 linting。Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.
Use this skill when editing or creating CLI output, logging, warnings, error messages, progress indicators, or diagnostic summaries in the APM codebase. Activate whenever code touches console helpers (_rich_success, _rich_warning, _rich_error, _rich_info, _rich_echo), DiagnosticCollector, STATUS_SYMBOLS, CommandLogger, or any user-facing terminal output — even if the user doesn't mention "logging" or "UX" explicitly.