Skill UI

This skill automatically collects raw activity data (merged PRs, issues, commits, and optional Slack updates) from repositories. It formats this data using predefined templates, runs quality checks, and prepares a structured changelog draft. It can automatically commit the draft to a new branch and open a Pull Request (PR) for review, making it ideal for generating weekly or monthly release notes.

This skill provides comprehensive guidance on leveraging d3.js (Data-Driven Documents) to create sophisticated, interactive, and publication-quality data visualizations. It covers core concepts like data binding, implementing various chart types (network graphs, geo-maps, etc.), handling complex transitions, and ensuring responsiveness across modern JavaScript frameworks (React, Vue, Vanilla JS). Learn the standard workflow for scale definition, axis creation, and drawing custom visual elements with fine-grained control.

A comprehensive workflow designed to orchestrate the entire lifecycle of large-scale features, from initial requirement gathering and architectural design through multi-service implementation, rigorous testing, and modern deployment strategies (e.g., canary, blue-green). Use this skill when coordinating complex, cross-functional feature delivery across frontend, backend, and data teams, ensuring quality and consistency at every phase.

Automates CodeQL database builds, suite selection, and taint-aware scans to surface security and quality issues across supported languages, handling output directories, SARIF results, and extensions in one workflow.

This comprehensive template provides a production-grade reference architecture for CodeRabbit AI code reviews. It is designed to guide teams in establishing rigorous coding standards by defining specific, best-practice instructions for various layers (API, Database, Authentication, Frontend, CI/CD, Infrastructure). Use this as a robust starting point to build a comprehensive, automated, and secure code quality review process.

Guides data engineering patterns for ETL pipelines, Spark analytics, and quality checks, showcasing Python and SQL snippets to extract, transform, load, validate, and model sales data in modern warehouses.

This comprehensive workflow guides the entire database lifecycle, from initial architecture design (covering both SQL and NoSQL) to advanced implementation, optimization, and maintenance. It covers crucial stages like schema design, implementing migrations, optimizing query performance, developing robust data pipelines (using tools like Airflow and dbt), and managing data quality and operations. Ideal for engineers building complex, production-grade data systems.

A comprehensive, end-to-end workflow bundle guiding developers through the entire software development lifecycle. It covers project scaffolding, frontend development (React/Next.js), robust backend architecture, database design, rigorous testing (TDD, E2E), code quality review, and modern CI/CD deployment practices for web and mobile applications.

A comprehensive guide to implementing production-grade reliability patterns for web scraping pipelines using Firecrawl. Learn techniques such as circuit breakers, crawl fallbacks, content quality validation, and robust backoff strategies to handle timeouts, API failures, and inconsistent data quality, ensuring maximum uptime and data integrity.

Set up comprehensive CI/CD pipelines for PostHog integrations using GitHub Actions. This guide covers mocked unit tests (no API key needed), robust integration tests against a development PostHog project, and automated deployment annotations to mark releases in your PostHog timeline. Ideal for developers ensuring data quality and traceability across all environments.

A comprehensive skill set for building robust, type-safe, and highly performant Python 3.11+ applications. This specialist enforces best practices by generating type-annotated code, utilizing async/await patterns, structured dataclasses, and comprehensive error handling. The workflow mandates rigorous validation using tools like mypy (strict mode), black, ruff, and extensive pytest test suites to ensure code quality and reliability.

A comprehensive guide to modern React UI patterns. It covers best practices for managing application states, including determining when to use skeleton loaders vs. spinners, implementing robust error handling hierarchies (inline, toast, banner), and designing effective empty states. Essential for building high-quality, user-friendly components that handle asynchronous data gracefully.