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Found 2,778 Skills
Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I/O-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.
Ruby on Rails testing best practices for writing effective, maintainable test suites with RSpec. This skill should be used when writing, reviewing, or refactoring Rails tests to ensure proper test design, data management, and coverage patterns. Triggers on tasks involving RSpec specs, model tests, request specs, system tests, factory definitions, Capybara interactions, Sidekiq job tests, or test suite optimization. Complementary to rails-dev, ruby-optimise, and ruby-refactor skills.
Vitest 4+ testing with Vite. Use when configuring vitest.config.ts, writing unit/integration/browser tests, implementing mocks with vi.fn/vi.spyOn/vi.mock, setting up V8 or Istanbul coverage, or migrating from Jest or older Vitest workspace setups. Triggers on vitest, vitest.config.ts, vi.mock, browser mode, vitest/browser, projects, setupFiles, and toMatchScreenshot.
Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.
Manages Amazon DocumentDB end-to-end — serverless-on-8.0 cluster setup, TLS/VPC/driver config, flexible-schema and vector-search data modeling, MongoDB compatibility assessment, DMS-based migration, slow-query diagnosis, major version upgrades (4.0→5.0→8.0), Well-Architected reviews (41-check wa_review.py), cost estimation, and security hardening. Retrieve for every DocumentDB question and when the user asks to set up or migrate MongoDB to AWS — DocumentDB is AWS's MongoDB-compatible managed database. Triggers: JSON document store, document database, MongoDB on AWS, Nested fields, Lambda cannot connect, TLS handshake, VPC port 27017, IAM auth, Secrets Manager, encryption at rest, $graphLookup, flexible schema, COLLSCAN, compound index, DMS migration, CDC cutover, $vectorSearch, RAG, Global Clusters, DR replication, cost sizing, audit, health check, production-readiness.
Run Rivet entirely inside your own perimeter: single-binary or Docker Compose install, file system storage with no database infrastructure, and no outbound telemetry by default.
Build a multi-page Markdown wiki directory for a large software repository after reading and analyzing the whole codebase. Use when the agent is asked to create repository wiki documentation, onboarding docs, architecture guides, codebase tours, maintainer handbooks, or deep explanations of modules, core code paths, algorithms, design decisions, tradeoffs, tests, tooling, and operations for code learners or new maintainers. Optimized for large repositories with hundreds of thousands of lines of code where maintainers need broad coverage and deep subsystem documentation. Also supports optional Rspress/static documentation site setup when the user explicitly asks to publish or deploy the generated wiki. Supports Markdown output with Mermaid, Graphviz, and KaTeX where useful.
Implement centralized logging with ELK Stack, Loki, or Splunk for log collection, parsing, storage, and analysis across infrastructure.
Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry).
Use when configuring, writing, debugging, running, or migrating Vitest tests in JavaScript/TypeScript projects, including Vite, Vue, Nuxt, React, Next.js, Node libraries, workspaces, coverage, mocks, snapshots, flaky tests, and Jest migration.
Comprehensive guide for implementing Syncfusion React input components including Uploader, NumericTextBox, TextBox, TextArea, CheckBox, OTP Input and Signature and RangeSlider. Use this when building file upload UIs with async/chunk uploads, drag-and-drop functionality, numeric inputs with validation and formatting, text inputs with floating labels, custom adornments, form integration, accessibility compliance, and styling in React applications.
Generates original sports journalism articles by consuming real-time data from the sports-skills skills. Covers game previews, live reports, post-game, team analysis, and player profiles for all supported sports. Use when: the user asks to write, generate, create, or draft an article, preview, report, analysis, summary, or journalistic coverage about games, teams, players, scores, statistics, or sports results. Do not use when: the user only wants raw data without journalistic text — use the sport-specific skill directly (nfl-data, nba-data, football-data, etc.). Do not use when: the user wants to search for news published by third parties — use sports-news.