Skill UI

A comprehensive, phased guide detailing how to migrate your code review process to CodeRabbit. It covers the entire journey, from assessing current tools (like SonarCloud, DeepSource, or manual reviews) and mapping existing rules, through to running a parallel pilot phase and finally achieving full adoption. Ideal for engineering managers or DevOps teams planning a major CI/CD tool transition.

Monitor the effectiveness and adoption of AI code reviews using CodeRabbit. This skill calculates critical metrics including review coverage percentage, time-to-first-review, and comment acceptance rates. It is ideal for building organizational dashboards to maintain high code quality and optimize development workflows.

A comprehensive guide for administrators and teams on setting up Cursor for enterprise use. It covers organizational plan selection (Pro, Business, Enterprise), advanced features like SSO, SCIM provisioning, and Privacy Mode enforcement. The guide details setting up roles, managing usage analytics, defining shared project rules via Git, and providing a structured onboarding checklist to ensure seamless adoption and secure coding practices across the organization.

This guide provides a comprehensive, production-grade architecture for implementing analytics using PostHog. It covers best practices for event taxonomy design, SDK layering (client and server), feature flag management, and integrating the data pipeline (CDP). Ideal for tracking user lifecycle, feature adoption, and revenue in complex web applications.

Integrate Sentry into your CI/CD pipeline to achieve end-to-end release tracking. This skill guides you through using the Sentry CLI and SDK to record deployments, associate commits, upload source maps, and monitor crucial release health metrics. Key functions include detecting regressions, calculating crash-free session rates, and tracking user adoption curves for every deployed version.

This skill is designed to retrieve and display detailed usage metrics for GitHub Copilot within organizations and enterprises. Users can analyze adoption rates, total suggestions, acceptance rates, and chat interactions, broken down either by the entire organization/enterprise or on a per-user basis. It supports querying data for specific historical dates using GitHub CLI and REST API.

Guides preserving React 18 concurrent patterns during migration to React 19 while outlining post-migration adoption of new APIs like use(), Actions, useOptimistic, and Suspense data fetching so concurrent behavior stays intact.

This skill generates a comprehensive Technology Radar document following the ThoughtWorks format. It provides a structured framework for an engineering team to evaluate and categorize all significant technologies (tools, languages, platforms, techniques) into four quadrants (Adopt, Trial, Assess, Hold). It helps establish a shared, explicit technology strategy, detailing adoption rationales, decision trails, and maintenance processes for consistent engineering choices.

Utilize this skill to assess the health, adoption, and performance metrics of published EAS Updates directly via the CLI. It reports critical data points including crash rates, unique user counts, install volumes, payload size, and the split between embedded and OTA users, making it essential for post-publish monitoring and regression detection.

This skill covers the principles of building Software Development Kits (SDKs) that provide an exceptional developer experience (DX). Learn how to design intuitive APIs, utilize progressive disclosure, establish sensible defaults, and implement actionable, guiding error messages. The focus is on driving product adoption by making the developer's workflow seamless and productive. Ideal for product managers, technical leads, and API designers.

This skill executes the metaharness score to provide a comprehensive, multi-dimensional readiness scorecard for a codebase. It assesses critical dimensions like compilation confidence, task coverage, tool safety, and memory usefulness. Use it to evaluate if a repository is prepared for harness adoption before initiating development tools or framework setup.

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.