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Found 2,635 Skills
Check and configure code coverage thresholds and reporting
Configure and use Railway's S3-compatible storage buckets. Use when implementing file uploads with Railway storage, setting up S3 clients for Railway, or troubleshooting Railway bucket access issues.
[Pragmatic DDD Architecture] Guide for Next.js 16 Proxy (formerly middleware), app router segments, layout composition, i18n URL localization, cookie management, and redirect strategies between public and protected routes. Use when updating proxy.ts, configuring public vs private environments, modifying the [locale] vs /dashboard routing structure, and appending headers.
Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (formerly neo4j-genai). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever), retrieval_query Cypher fragments, query_params, pipeline wiring (GraphRAG + LLM), embedder setup, index creation, and LangChain/LlamaIndex integration. Does NOT handle KG construction from documents — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.
Diagnose ClickHouse disk usage, compression efficiency, part sizes, and storage bottlenecks. Use for disk space issues and slow IO.
Integrate existing Fragno fragments into applications: install fragment packages, configure server instances (and database adapters if required), mount server handlers for each framework, create client-side integrations, and use hooks/composables. Use when asked to wire a fragment into the user's application.
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.
Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking. Use this skill when you need to search the web for current information, research topics across multiple sources, or gather context from the internet without using external APIs. This skill teaches effective use of RAG-based web search with DuckDuckGo, Google, and multi-engine deep research capabilities.
Reading and writing data with Pandas from/to cloud storage (S3, GCS, Azure) using fsspec and PyArrow filesystems.
Define the structure and organization of storage functions within a project.
Defragment and reorganize agent memory files: split bloated files, merge duplicates, remove stale information, and restructure the memory hierarchy. Use when memory files have grown unwieldy, contain redundancies, or need reorganization. Run periodically (weekly) or on demand.
Use when storing project artifacts in basic memory storage.