Skill UI

Automates penetration test planning tasks by guiding workflows, validating outputs, and generating production-ready artifacts aligned with compliance and threat-modeling needs.

Automates guidance for Performance Lighthouse Runner tasks in frontend projects, offering best practices, production-ready snippets, and validations whenever performance or accessibility topics arise.

This guide provides comprehensive instructions for deploying applications powered by the Perplexity Sonar API. It covers setup for major cloud platforms like Vercel, Google Cloud Run, and Docker. Users can deploy production-ready endpoints, manage required secrets, implement edge functions, and utilize caching mechanisms (e.g., Redis) to ensure high availability and performance in production environments.

This skill provides production-ready patterns for integrating with the Perplexity Sonar API in both TypeScript and Python. It covers best practices for creating robust wrappers around the standard OpenAI client, including dedicated handling for citations, search results, and related questions. It also includes essential utilities like implementing exponential backoff for reliable retries. Use this when setting up complex search-augmented generation workflows or standardizing team coding practices around Perplexity usage.

Guides users through plantuml diagram generator tasks, covering activation triggers, best practices, and production-ready code for visual content work.

Automates Plotly chart creation by offering step-by-step guidance, best-practice patterns, and production-ready code or configuration whenever visual content, diagrams, or presentations need charts.

This guide provides robust, production-ready strategies for integrating with the PostHog API. It teaches developers how to handle API rate limits (429 errors) gracefully using exponential backoff and retry logic. Furthermore, it covers implementing request queuing for burst protection, optimizing with data caching, and monitoring rate limit headers, ensuring stable and reliable data ingestion regardless of traffic volume.

A comprehensive guide to implementing production-ready patterns for PostHog analytics integration. This includes establishing singleton clients for both server-side (Python/Node) and client-side environments. It provides type-safe event capture utilities, React hooks for handling feature flags (including variants), and the full Next.js App Router setup for robust pageview tracking. Patterns are available for both TypeScript/JavaScript and Python environments.

Prediction Monitor Assistant automates machine learning deployment monitoring tasks, offering step-by-step guidance, best-practice patterns, production-ready code and configuration generation, and validation to keep serving and inference workflows reliable.

Guides and automates Prefect Flow Builder tasks for data pipelines, offering best-practice patterns, production-ready code, configuration templates, and validation advice whenever you mention the tool or request pipeline help.

Automates guidance for outlining presentation slides within visual content workflows, following industry best practices and generating production-ready outputs.

Auto-activating skill that delivers step-by-step guidance, best-practice validation, and production-ready code for crafting process flow visuals, diagrams, and documentation whenever you invoke the process flow generator.