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Found 1,837 Skills
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google Workspace RAG, or other RAG products like gRAG.
Use the Data Analysis Agent to perform natural language business analytics, auto-generate SQL queries, create visualizations, and produce business insights from Excel/CSV/databases
This skill should be used when the user wants to interact with their paper database — listing papers, searching content, showing paper details, adding papers, or exporting context. Matches queries like "search papers for X", "add this arXiv paper", "show equations from paper Y", "what papers do I have". Prefer CLI over MCP RAG tools for direct lookups.
Create Railway projects, services, and databases with proper configuration. Use when user says "setup", "deploy to railway", "initialize", "create project", "create service", or wants to deploy from GitHub. Handles initial setup AND adding services to existing projects. For databases, use railway-railway-database skill instead.
AWS cost optimization and FinOps workflows. Use for finding unused resources, analyzing Reserved Instance opportunities, detecting cost anomalies, rightsizing instances, evaluating Spot instances, migrating to newer generation instances, implementing FinOps best practices, optimizing storage/network/database costs, and managing cloud financial operations. Includes automated analysis scripts and comprehensive reference documentation.
Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.
Analyzes and optimizes SQL/NoSQL queries for performance. Use when reviewing query performance, optimizing slow queries, analyzing EXPLAIN output, suggesting indexes, identifying N+1 problems, recommending query rewrites, or improving database access patterns. Supports PostgreSQL, MySQL, SQLite, MongoDB, Redis, DynamoDB, and Elasticsearch.
Search and discover AI skills using the SkillsMP API. Supports keyword search and AI semantic search powered by Cloudflare AI. Use when users want to find AI skills, search for specific capabilities, or explore community-built AI tools. Triggers on "search skills", "find AI skills", "look up skills", "skills marketplace", "AI skills database".
bkend.ai file storage expert skill. Covers single/multiple/multipart file upload via Presigned URL, file download (CDN vs Presigned), 4 visibility levels (public/private/protected/shared), bucket management, and file metadata. Triggers: file upload, download, presigned, bucket, storage, CDN, image, 파일 업로드, 다운로드, 버킷, 스토리지, 이미지, ファイルアップロード, ダウンロード, バケット, ストレージ, 文件上传, 下载, 存储桶, 存储, carga de archivos, descarga, almacenamiento, cubo, telechargement, televersement, stockage, seau, Datei-Upload, Download, Speicher, Bucket, caricamento file, scaricamento, archiviazione, bucket Do NOT use for: database operations (use bkend-data), authentication (use bkend-auth).
Local dev environments with Docker Compose - multi-service setups, databases, hot reload, debugging. Use when: docker compose, local dev, postgres container, redis local, dev environment.
Provision new NixOS servers on Proxmox for this nix flake project. Guides through the complete workflow: creating Proxmox LXC containers, SSH setup, Colmena configuration (init/full pattern), and application deployment with nginx proxy, PostgreSQL, and container images. Use when: (1) Creating a new server/container on Proxmox, (2) Setting up a new NixOS host with Colmena, (3) Deploying applications with nginx SSL proxy and/or PostgreSQL database, (4) Adding new container images to the repository.
This skill should be used when building data processing pipelines with CocoIndex v1, a Python library for incremental data transformation. Use when the task involves processing files/data into databases, creating vector embeddings, building knowledge graphs, ETL workflows, or any data pipeline requiring automatic change detection and incremental updates. CocoIndex v1 is Python-native (supports any Python types), has no DSL, and is currently under pre-release (version 1.0.0a1 or later).