Operate Milvus vector database with pymilvus Python SDK. Use when the user wants to connect to Milvus, create collections, insert vectors, perform similarity search, hybrid search, full-text search, manage indexes, partitions, databases, or RBAC via Python code.
Guide for using qi, a local knowledge search CLI for macOS and Linux. Use this skill when the user wants to index documents into a searchable knowledge base, search or retrieve from one, configure qi's collections or embedding providers (Ollama, OpenAI-compatible), choose between its search modes (BM25, hybrid…
Turn any folder of files (code, docs, papers, images, video) into a queryable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPHREPORT.md. Use when asked to analyze a codebase, understand architecture, map dependencies…
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.
Use when the user wants to add a Markdown document to the knowledge base, ingest a whole directory of .md files, or initialize the knowledge base from a Git repo. Triggers on "ingest this doc", "add to knowledge base", "import these markdown files", "set up the knowledge base from this repo" — even when the user…
Run this as the FINAL review step once you have finished a UI change you can load in a browser — it is the visual-regression gate, run once at the end (not after every edit), and the change is not done until it reports score: 100. It drives a real browser over the page, auto-detects its relevant elements (no…
Compress images, long prompts, conversations, and RAG documents locally via Ollama before consuming LLM tokens. Reduces token usage by 80% on images and 85% on text. Use proactively when: the user uploads images, processes long context, works with multiple RAG sources, or when approaching token limits.
Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.
Semantic search, context management, and document indexing via OpenViking. Use when the user asks to: index/import documents or files into a knowledge base, perform semantic search across indexed content, browse or explore indexed resources, get summaries/overviews of indexed documents, manage an OpenViking instance…
Comprehensive AI agent building skill merging Perplexity Computer's skill creation, webserver, and automation capabilities with Claude Code's agent orchestration, MCP server building, RAG system construction, subagent coordination, parallel agent dispatching, prompt optimization, and execution planning. Covers…
A document question-answering setup that finds relevant passages in your files and uses a language model to generate answers. It accepts PDF, DOCX, TXT, and Markdown files, and can store searchable document collections.
A Russian-language skill for searching a local knowledge base built from documents and other sources such as web pages, YouTube, audio, and Obsidian notes.
Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPHREPORT.md.
Use when (re)indexing a Sweet Search project. Runs the full-profile indexer with GPU model prewarming (CoreML cascade on M3+, candle Metal on M1/M2, ORT CPU elsewhere), kills ORT CPU models during indexing to avoid memory contention, and rewarms them for query readiness on completion. Incremental runs under 20 files…
A mock technical interview focused on the Modular RAG MCP Server. It can optionally use your resume, ask follow-up questions, and produce a saved report with scores, sample answers, and feedback on how you present your experience.
Use when the user needs self-hosted or local Chroma for semantic search, including ChromaClient, HttpClient, or Python EphemeralClient, local persistence, Docker or chroma run, or OSS Chroma without Chroma Cloud features.
Refresh the operational-to-ontology projection, validate class and property contracts, enrich changed nodes for semantic search, and inspect Oracle Scheduler evidence.
Use when building a RAG pipeline that ingests PDFs, Excel, CSV, or images — especially when debugging silent data loss, choosing between OCR tools, or handling edge cases like scanned pages, merged cells, or embedded charts.
Zvec vector database development assistant. Use this skill when users need to develop vector search applications based on zvec, build RAG systems, implement semantic search, or handle vector data storage and querying. Suitable for Python and Node.js development environments, providing complete technical guidance from…
Create and operate ChatDOC Studio knowledge bases through pdrouter using a Bearer API key and JavaScript helpers. Use when Codex needs to upload one or more PDF/DOC/DOCX files, skip failed files without aborting the whole job, create a knowledge base from successful uploads, or call the ChatDOC Studio knowledge-base…
★not rated 16 1mo agoA92 tokens
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: