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Found 1,317 Skills
Build AI-powered Ruby applications with RubyLLM. Full lifecycle - chat, tools, streaming, Rails integration, embeddings, and production deployment. Covers all providers (OpenAI, Anthropic, Gemini, etc.) with one unified API.
Use this skill for Next.js App Router patterns, Server Components, Server Actions, Cache Components, and framework-level optimizations. Covers Next.js 16 breaking changes including async params, proxy.ts migration, Cache Components with "use cache", and React 19.2 integration. For deploying to Cloudflare Workers, use the cloudflare-nextjs skill instead. This skill is deployment-agnostic and works with Vercel, AWS, self-hosted, or any platform. Keywords: Next.js 16, Next.js App Router, Next.js Pages Router, Server Components, React Server Components, Server Actions, Cache Components, use cache, Next.js 16 breaking changes, async params nextjs, proxy.ts migration, React 19.2, Next.js metadata, Next.js SEO, generateMetadata, static generation, dynamic rendering, streaming SSR, Suspense, parallel routes, intercepting routes, route groups, Next.js middleware, Next.js API routes, Route Handlers, revalidatePath, revalidateTag, next/navigation, useSearchParams, turbopack, next.config
Normalize and validate deployment version under Makefile-first workflow. Use when reading or validating version for test/prod/custom environments before make-based deployment.
Build complete demo projects from scratch. Takes a project description (presentation website, shop, dashboard, SaaS, portfolio, etc.) and scaffolds a full working Next.js + Tailwind CSS 3.4 app ready for Vercel deployment. Supports optional database integration when a DATABASE_URL is provided. Use when the user wants to build a demo, create a project, scaffold an app, prototype something, or spin up a quick site.
Rollback failed deployments, restore previous versions, and handle deployment emergencies. Use when deployments fail, critical bugs are discovered in production, performance degrades after deployment, or emergency recovery is needed.
Complete fullstack development mastery covering modern web architectures, automation tools, AI integration, and production deployment practices
Playwright browser automation API, web scraping, and tooling. Covers locator strategies, assertions, API testing, stealth mode, anti-bot bypass, authenticated sessions, screenshots/PDFs, Docker deployment, configuration, debugging, and MCP integration with AI agents. Prevents documented errors including CI timeout hangs, extension testing failures, and navigation issues. Use when automating browsers, scraping protected sites, bypassing bot detection, generating screenshots/PDFs, configuring Playwright Test, troubleshooting Playwright errors, or learning Playwright API patterns. For E2E test architecture, Page Object Models, CI sharding strategies, or test organization patterns, use the e2e-testing skill instead.
Use this skill first whenever the user asks about SigNoz instrumentation, OpenTelemetry setup, querying, dashboards, alerts, troubleshooting, self-hosted deployment, API endpoints, auth headers, or where to find anything in SigNoz docs.
Fast-track an urgent fix through a streamlined pipeline. Skips Product/Feature Council, applies the fix, runs a focused review, and creates a PR with optional Deployment Council. Use for production bugs, security patches, or critical regressions that cannot wait for the full planning pipeline.
Expert guidance for Spring Boot application development with best practices for RESTful APIs, testing, security, and deployment
Comprehensive Cline SDK skill for building AI agents. Covers the Agent runtime, ClineCore sessions, custom tools, plugins, events, LLM providers, scheduling, multi-agent teams, and production deployment. Use for any task involving @cline/sdk or its sub-packages.
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.