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Found 261 Skills
Convert Markdown documents into beautiful, comfortable, well-structured, directly openable HTML long-form reports. Suitable for converting .md files to HTML, standardizing the layout of research/industry/survey documents, generating single-file web reports for offline sharing, embedding or verifying local images, fixing misaligned columns caused by Markdown table separators, or optimizing reading margins, image presentation, directory navigation, table responsiveness, and print styles of existing HTML reports.
Deploy, operate, and integrate the VSS 3.2 GA RT-Embed Video Embedding microservice. Covers Docker Compose bring-up, GPU and storage prerequisites, the `/v1` REST API (file uploads, text and video embeddings, live RTSP streams, health and metrics), Redis/Kafka/OTel integration, common failure modes, and teardown.
Guidance for text embedding retrieval tasks using sentence transformers or similar embedding models. This skill should be used when the task involves loading documents, encoding text with embedding models, computing similarity scores (cosine similarity), and retrieving/ranking documents based on semantic similarity to a query. Applies to MTEB benchmark tasks, document retrieval, semantic search, and text similarity ranking.
Expert OpenRouter API assistant for AI agents. Use when making API calls to OpenRouter's unified API for 400+ AI models. Covers chat completions, streaming, tool calling, structured outputs, web search, embeddings, multimodal inputs, model selection, routing, and error handling.
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.
Implements JWT SSO authentication for Metabase embedding in a project. Supports all embedding types that use SSO — Modular embedding (embed.js web components), Modular embedding SDK (@metabase/embedding-sdk-react), and Full app embedding (iframe-based). Creates the JWT signing endpoint, configures the frontend auth layer, and sets up group mappings. Use when the user wants to add SSO/JWT auth to their Metabase embedding, implement user identity for embedded analytics, set up JWT authentication for Metabase, or connect their app's authentication to Metabase embedding.
Neo4j Graph Data Science (GDS) plugin — graph projection, algorithm execution, execution modes (stream/stats/mutate/write), memory estimation, and the GDS Python client (graphdatascience v1.21). Use when running gds.pageRank, gds.louvain, gds.wcc, gds.fastRP, gds.knn, gds.betweenness, gds.nodeSimilarity, or any gds.* procedure; projecting named in-memory graphs with gds.graph.project or graph.project; chaining algorithms with mutate mode; computing node embeddings for ML; building recommendation systems with FastRP + KNN. Also triggers on GraphDataScience, GdsSessions, graph catalog operations, ML pipelines, node classification, link prediction. Does NOT cover Aura Graph Analytics serverless sessions — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver skill.
HNSW vector search with RuVector embeddings for 150x-12500x faster semantic retrieval
Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).
Enforce allowed and forbidden conversation topics using semantic embedding similarity with session-aware drift detection
Go interfaces, type assertions, type switches, and embedding from Effective Go. Covers implicit interface satisfaction, comma-ok idiom, generality through interface returns, interface and struct embedding for composition. Use when defining or implementing interfaces, using type assertions/switches, or composing types through embedding.