Skill UI

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.

This skill specializes in debugging distributed systems and analyzing production incidents. It helps identify root causes of errors using observability tools, structured logging, and distributed tracing. You will implement fixes, establish preventive measures, and improve system reliability. Suitable for investigating recurring errors and designing error handling improvements.

This skill guides the implementation of comprehensive error monitoring and observability solutions. It helps in setting up robust error tracking systems, configuring detailed alerts, implementing structured logging, and establishing workflows to ensure teams can quickly detect, group, and resolve production issues efficiently.

Coordinate a bounded code review from multiple perspectives including correctness, authorization, and performance. This skill describes a review procedure for defined diffs or subsystems. It involves reading diffs, choosing relevant perspectives, recording findings with exact locations, reproducing issues, and resolving disagreements through code or tests. It is a read-only review process that does not install orchestration engines or prove compliance.

Systematic error analysis and resolution for debugging distributed systems, investigating production incidents, and implementing observability solutions. Provides root-cause analysis, structured logging, distributed tracing, and preventive measures to improve system reliability.

Expert AI-assisted debugging workflow for modern software systems. Covers triage, observability data collection, hypothesis generation, strategy selection, instrumentation, root cause analysis, fix implementation, validation, and prevention. Ideal for developers facing complex production issues.

This skill provides guidance on implementing robust error handling strategies to build highly resilient applications. It covers advanced patterns such as circuit breakers, retries, and handling asynchronous errors, ensuring that the application fails gracefully and provides excellent debugging experiences for both developers and end-users.

An advanced debugging skill designed for AI agents. It utilizes gdb-cli to provide intelligent assistance for C/C++ programs, enabling the analysis of core dumps, debugging live processes, and investigating complex issues like crashes, deadlocks, and memory corruption. It correlates runtime state inspection with source code context for deeper insights.

Automate software development workflows directly within GitHub repositories. Configure CI/CD pipelines, build, test, and deploy applications efficiently. Manage secrets, caching, artifacts, and reusable workflows to streamline DevOps processes. Supports self-hosted runners and debugging for robust deployment strategies.

Expert guidance for designing, debugging, and securing production-grade GitHub Actions CI/CD workflows, including reusable workflows, matrix builds, self-hosted runners, OIDC authentication, caching, environments, secrets, and release automation.

A comprehensive guide to production-grade concurrency patterns in Go. It details the proper use of goroutines, channels, sync primitives, and context management. Use this skill when building highly concurrent applications, implementing worker pools, managing goroutine lifecycles, or debugging race conditions in Go.

Build, deploy, and maintain machine-learning applications on Hugging Face Spaces using Gradio, Docker, or Static SDKs, with ZeroGPU and dedicated hardware, model loading, debugging, and inference providers.