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 systematic workflow designed to significantly improve the reliability and performance of existing AI agents. It covers detailed performance analysis, including gathering metrics (e.g., task success rate, latency), classifying failure modes, and advanced prompt engineering techniques (like Chain-of-Thought and Constitutional AI). The process culminates in structured A/B testing and controlled deployment, ensuring measurable, robust improvements.

An advanced AI-driven framework for holistic performance engineering of multi-agent systems. It provides coordinated profiling, workload distribution, and cost-aware orchestration across various domains (database, application, frontend). Use this tool to identify bottlenecks, improve throughput, enhance reliability, and manage resource constraints in complex agent workflows.

Helps teams ship reliable AI-powered products by detailing LLM integration patterns, RAG architecture, scalable prompt engineering, UX resilience, and cost-aware design.

Defines how to build AI wrapper products that solve specific customer problems, covering prompt engineering, API cost control, usage metering, and defensible UX so AI becomes a paid, reliable feature.

Defines how to build AI wrapper products that solve specific customer problems, covering prompt engineering, API cost control, usage metering, and defensible UX so AI becomes a paid, reliable feature.

This skill provides comprehensive tools for managing Apollo.io contact data governance and compliance. It supports critical data lifecycle tasks, including executing GDPR Subject Access Requests (SAR), enforcing the Right to Erasure, implementing data retention policies, and performing field-level encryption. Ideal for legal, data privacy, or engineering teams handling PII.

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.

This guide provides comprehensive patterns and best practices for working with ClickHouse, a high-performance OLAP database. It covers advanced topics such as MergeTree engine design, efficient query optimization (filtering, aggregations, window functions), bulk/streaming data ingestion, materialized view creation, and performance monitoring techniques, ensuring optimal performance for large-scale analytical workloads.

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.