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Found 2,138 Skills
Emulated Stripe API for local development and testing. Use when the user needs to process payments locally, test checkout flows, create customers, manage products and prices, handle payment intents, work with webhooks, or use the Stripe SDK without hitting real Stripe servers. Triggers include "Stripe API", "emulate Stripe", "test payments locally", "checkout flow", "payment intent", "Stripe webhook", "Stripe SDK", "STRIPE_API_KEY", or any task requiring a local Stripe API.
Choose and create the right Neon branch type for testing and development. Use when users ask about Neon branching, migration testing with real data, isolated test environments, schema-only branch workflows for sensitive data, or branch creation via Neon CLI or Neon MCP. Triggers include "Neon branch", "test migrations safely", "branch production data", "schema-only branch", "reset branch" and "sensitive data testing".
Integrate a HuggingFace Computer Vision model into the NVIDIA TAO Toolkit ecosystem (tao-core config, tao-pytorch trainer, tao-deploy TensorRT pipeline). Use when the user asks to "integrate a HuggingFace model into TAO", "add an HF model to TAO Toolkit", "wire a HuggingFace ViT/DETR/ SegFormer into tao-pytorch", "build a TAO trainer + deploy pipeline for an HF CV model", or pastes a HuggingFace model URL/ID and wants it turned into a TAO model. Covers the full 7-phase loop: prerequisites check, HuggingFace inspection and validation, codebase exploration, tao-core configuration and native trainer implementation, ONNX export plus TensorRT deploy integration, packaging and L0 testing, container-based end-to-end validation, and (conditional) accuracy/latency tuning. Supports classification, object detection, semantic / instance / panoptic segmentation, zero-shot detection, and depth estimation.
Master the art of calls-to-action that convert. Direct CTAs, transitional CTAs, button copy, and microcopy that turns readers into customers. Use when: Writing button text for landing pages and emails; Creating CTAs for different stages of awareness; Designing click-worthy microcopy; A/B testing CTA variations; Building email sequences with graduated CTAs
This skill should be used when the user asks to "optimize TypeScript performance", "speed up tsc compilation", "configure tsconfig.json", "fix type errors", "improve async patterns", or encounters TS errors (TS2322, TS2339, "is not assignable to"). Also triggers on .ts, .tsx, .d.ts file work involving type definitions, module organization, or memory management. Does NOT cover TypeScript basics, framework-specific patterns, or testing.
Manages Apache Airflow operations including listing, testing, running, and debugging DAGs, viewing task logs, checking connections and variables, and monitoring system health. Use when working with Airflow DAGs, pipelines, workflows, or tasks, or when the user mentions testing dags, running pipelines, debugging workflows, dag failures, task errors, dag status, pipeline status, list dags, show connections, check variables, or airflow health.
This skill should be used when the user asks to "generate tests", "write unit tests", "analyze test coverage", "scaffold E2E tests", "set up Playwright", "configure Jest", "implement testing patterns", or "improve test quality". Use for React/Next.js testing with Jest, React Testing Library, and Playwright.
Production Fastify (TypeScript) patterns: schema validation, plugins, typed routes, error handling, security hardening, logging, testing with inject, and graceful shutdown
Writing Playwright E2E tests for tldraw. Use when creating browser tests, testing UI interactions, or adding E2E coverage in apps/examples/e2e or apps/dotcom/client/e2e.
Identify security vulnerabilities through SAST, DAST, penetration testing, and dependency scanning. Use for security test, vulnerability scanning, OWASP, SQL injection, XSS, CSRF, and penetration testing.
Automate QA regression testing with reusable test skills. Create login flows, dashboard checks, user creation, and other common test scenarios that run consistently.
This skill should be used when working with DSPy.rb, a Ruby framework for building type-safe, composable LLM applications. Use this when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers (OpenAI, Anthropic, Gemini, Ollama), building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.