Skill UI

This skill provides expertise in analyzing DWARF debug files and understanding the DWARF debug format standard versions 3 through 5. It is used for parsing DWARF information from compiled binaries, answering questions about the DWARF standard, writing or reviewing code that interacts with DWARF data, and verifying DWARF data integrity using tools like dwarfdump and llvm-dwarfdump.

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.

This skill enables reviewing defined code diffs from correctness, authorization, and performance perspectives. It supports bounded multi-agent review when authorized, ensuring findings are reproduced centrally. Users must pin repository paths and intended behavior. It does not install orchestration engines but provides a structured procedure for code quality assurance and security validation without overstepping user authority.

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.

This skill enables AI-assisted debugging of software failures using reproduction steps, logs, and traces. It helps generate hypotheses, analyze observability data, and verify fixes without fabricating evidence. Suitable for intermittent incidents and production issues requiring evidence-driven root cause analysis.

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.

This skill acts as an expert CI/CD diagnostician for GitHub Actions. It analyzes failed workflow logs and pipeline definitions to identify root causes like missing secrets, environment mismatches, or syntax errors. It provides precise YAML or code changes to fix the pipeline, optimize performance, and update deprecated actions while ensuring security best practices.

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.

This skill operates HyperExecute end-to-end for TestMu AI and LambdaTest cloud test execution. It helps analyze projects, create and validate YAML configurations, run CLI jobs, debug failures, and integrate CI/CD pipelines. It ensures secure credential handling and provides troubleshooting support for test automation workflows.