Skill UI

A specialized skill for architecting, building, and productionizing complex Agent Development Kit (ADK) agents and multi-agent systems. This process emphasizes best practices, including clean module structure, robust tool integration, comprehensive regression testing, and deployment automation for reliable, shippable AI solutions. Use this when requiring the creation of structured, enterprise-grade agent code.

A comprehensive set of architectural and coding standards for building high-reliability, observable, and maintainable backend services. It mandates the use of layered architecture, explicit error handling (Sentry), robust validation (Zod), and strict dependency management for Node.js, Express, and TypeScript projects.

A comprehensive technical guide detailing essential patterns and techniques for analyzing compiled binaries. It covers foundational assembly concepts across x86-64, ARM architectures, and provides structured examples for function prologues, calling conventions, control flow (loops, branches), data structure access, and common arithmetic/string operations. Essential for reverse engineering and low-level systems programming.

Designs and executes chaos experiments, failure-injection frameworks, and game days for distributed systems, providing runbooks, manifests, rollback plans, and continuous resilience automation in CI/CD.

A comprehensive guide for engineering teams to manage critical incidents related to Clerk authentication and SaaS platform outages. It covers full incident lifecycle procedures, including initial triage, emergency auth bypass implementation, key rotation, session recovery scripts, and post-incident root cause analysis. Use this when facing production authentication failures or security breaches.

This expert analyzes and refactors provided code based on clean code principles, SOLID design patterns, and modern software engineering best practices. It is designed to significantly improve the quality, maintainability, and performance of complex, messy, or poorly structured codebases, ensuring the resulting code is robust, scalable, and easy to maintain.

This expert analyzes and refactors existing codebases to enhance quality, maintainability, and performance. It specializes in applying clean code principles, SOLID design patterns, and modern software engineering best practices. Use it to clean up technical debt, simplify complex modules, or prepare a codebase for new feature development.

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.

Guides chaos engineering experiments by defining objectives, choosing tools such as Chaos Mesh, Gremlin, Toxiproxy, or AWS FIS, injecting failures, monitoring metrics, and recommending fixes to harden services and APIs.

A comprehensive, AI-assisted methodology for diagnosing complex software errors in production environments. This skill guides users through the entire debugging lifecycle, from initial triage and gathering observability data (Sentry, DataDog, Jaeger) to generating hypotheses, selecting appropriate debugging strategies (Time-Travel, Chaos Engineering), and performing AI-powered root cause analysis. It culminates in proposing validated fixes and prevention measures.

A comprehensive toolkit for monitoring the health and performance of Fireflies.ai integration. It tracks crucial metrics such as API connectivity, webhook delivery status, transcript processing latency, and seat utilization. Built using Prometheus metrics and suitable for Grafana dashboards, ensuring reliable data pipeline monitoring for engineering teams.

This resource provides production-ready, type-safe GraphQL client patterns for integrating with the Fireflies.ai API in both TypeScript and Python. It addresses critical engineering concerns such as robust error handling, managing singletons, implementing multi-tenant support, and ensuring structured data validation using best practices.