Skill UI

This skill simulates an expert performance engineer capable of diagnosing complex bottlenecks across the entire technology stack. It covers modern observability techniques (OpenTelemetry, APM), advanced profiling (CPU, Memory, I/O), and sophisticated load testing strategies (k6, Chaos Engineering). Use this when optimizing scalability, reducing latency, improving throughput, or architecting highly reliable, performant distributed systems in cloud environments.

Guides engineers through bundle analysis, lazy loading, image optimization, caching headers, virtualized lists, and Core Web Vitals monitoring so web apps stay fast and scalable for production traffic.

Automates web application penetration testing by leveraging the penetration-tester plugin to scan OWASP Top 10 threats, suggest exploitation techniques, and produce actionable vulnerability reports for pentest or vulnerability assessment requests.

Automates vulnerability scans for applications and APIs via the security-test-scanner plugin, covering OWASP Top 10, SQL injection, XSS, CSRF, authentication, and authorization weaknesses while generating detailed remediation reports.

A comprehensive guide to implementing Perplexity's API integration at various scales. It provides three validated architectural blueprints—Direct Widget, Cached Research Layer, and Multi-Query Pipeline—allowing developers to choose the optimal structure based on required query volume, latency goals, and complexity. Learn how to scale from simple search widgets to automated, high-volume research pipelines.

This skill provides comprehensive guidance on load testing and capacity planning for the Perplexity Sonar API. Users learn how to simulate high traffic volumes, respecting strict rate limits (like 50 RPM), benchmark latency, and estimate operational costs. It is essential for performance engineering, capacity architecture design, or stress testing APIs at scale.

This skill enables the development of robust, event-driven pipelines interacting with the Perplexity Sonar API. Since Perplexity lacks native webhooks, this system simulates event patterns using advanced techniques: Streaming Server-Sent Events (SSE) for real-time data, job queues (like BullMQ/SQS) for asynchronous batch research, and cron-triggered monitoring for scheduled news and trend alerts. It provides patterns for building complex, reliable, and scalable AI applications.

A specialist skill for writing robust, maintainable, and scalable End-to-End (E2E) tests using Playwright. This guide covers setting up test infrastructure, implementing Page Object Model (POM), using role-based selectors, handling API mocking, and debugging flaky tests. Ideal for integrating quality checks into CI/CD pipelines and performing complex browser automation.

Automates scaffolding of new Claude Code plugins within the claude-code-plugins repo, creating directories, plugin.json, READMEs, LICENSE, commands/agents/skills/MCP assets, and marketplace entries before validating schemas and syncing the catalog.

This guide details the standardized directory structure and best practices for building robust and maintainable plugins for Claude Code. It covers critical components including the plugin manifest (plugin.json), commands, agents, skills, and hooks. Use this skill when you need to scaffold a new plugin, understand the required component organization, or configure the plugin's architecture according to best practices.

This skill performs deep, line-by-line scanning of an entire codebase to understand its architecture, patterns, and dependencies. It systematically identifies critical issues across security vulnerabilities (SQL injection, secrets), performance bottlenecks (N+1 queries, caching), architectural flaws, and code quality gaps. It then automatically refactors and fixes all identified issues, transforming the code into professional, production-grade, enterprise-level quality.

This skill acts as an expert architect to generate complete, production-ready Python project structures. It supports various modern architectures, including FastAPI, Django (full-stack), standalone libraries, and CLI tools. It ensures adherence to best practices, includes dependency management (uv, pip), type hinting, and comprehensive testing skeletons.