Skill UI

Automated skill providing code splitting guidance for frontend development, covering React/Vue/CSS, modern performance and accessibility best practices, and producing validated, production-ready configurations.

Guides React Server Components workflows, streaming SSR, code splitting, bundle/image/font optimizations, and Core Web Vitals monitoring to keep Next.js frontends fast and responsive.

This comprehensive guide details the end-to-end process of building Retrieval-Augmented Generation (RAG) pipelines using LangChain. It covers critical steps: loading diverse document types (PDF, CSV, TXT), splitting text into manageable chunks, generating high-quality embeddings (OpenAI), storing them in various vector databases (FAISS, Pinecone), and finally constructing the full runnable chain for accurate and context-aware querying.

This comprehensive guide outlines best practices for developing modern, scalable applications using the Next.js App Router. It covers critical architectural patterns, including the decision-making process between Server and Client Components, advanced data fetching strategies (caching, revalidation), file-system routing principles, API route handling, and performance optimizations like image and bundle splitting. Essential knowledge for building robust full-stack React applications.

This skill coordinates the complex parallel execution of end-to-end, integration, and unit tests across multiple frameworks (e.g., Jest, Playwright, pytest) and CI/CD environments. It manages test splitting, worker allocation, tiered execution strategies, and aggregates results into unified reports. Ideal for optimizing the testing lifecycle and ensuring robust quality assurance in large-scale, multi-component applications. Key functions include intelligent retry logic for flaky tests and building CI pipelines (e.g., GitHub Actions matrix).

Comprehensive PDF toolkit for extracting text and tables, splitting/merging documents, filling forms, and generating new files via Python or CLI tools, enabling automated, large-scale document workflows in analytics, compliance, or reporting pipelines.

This guide provides a comprehensive overview of PDF manipulation techniques using both Python libraries (like pypdf, pdfplumber, reportlab) and command-line tools (like pdftotext, qpdf). It covers core functionalities such as merging, splitting, rotating, extracting text and structured tables, creating new PDFs, and performing OCR on scanned documents, making it essential for automation workflows.

A comprehensive guide covering the entire web performance optimization lifecycle. Learn how to measure Core Web Vitals (LCP, INP, CLS), identify bottlenecks using specialized tools like Lighthouse and DevTools, and apply advanced techniques such as code splitting, lazy loading, and memory leak fixes to ensure fast, stable, and high-quality user experiences.

Automates splitting datasets into training, validation, and test subsets by generating and running Python code so you can quickly prepare data for machine learning model training and evaluation.

Break large user stories, epics, or features into smaller, independently deliverable stories using systematic splitting patterns so agile teams can reduce risk, improve estimation, and keep value flowing when backlog items grow too big.

A comprehensive guide and methodology for identifying, measuring, and fixing performance bottlenecks across the entire stack. This skill covers optimizing database queries (e.g., N+1 problems, missing indexes), improving API efficiency (caching, parallel processing), and enhancing frontend performance (memoization, code splitting, image optimization). It emphasizes the critical 'Measure First' principle to ensure all optimizations are data-driven and effective.

Techniques for bypassing Web Application Firewalls using encoding, HTTP tricks, payload splitting, and SQLMap tamper scripts to deliver SQLi and XSS payloads during penetration tests and red-team engagements.