Skill UI

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.

This skill serves as an alias for the debugging-toolkit-smart-debug skill, redirecting users to the canonical implementation. It is designed to assist developers in working with debugging toolkits, providing guidance on safe next steps, key checks, and concrete outputs. Users should apply this when tasks clearly match the debugging scope, but must not treat the output as a substitute for environment-specific validation or expert review.

This skill offers expert guidance for AI-assisted debugging workflows and observability analysis. It covers root cause analysis, hypothesis generation, and production-safe debugging strategies using tools like Sentry and DataDog. Use it for troubleshooting errors, analyzing logs, and implementing validated fixes in development or production environments.

Design and implement CI/CD pipelines, Docker configurations, and deployment strategies with production-ready patterns. Covers GitHub Actions, multi-stage builds, and infrastructure automation for reliable software delivery. Includes security best practices and debugging workflows.

Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability. Provides guidance on log analysis, distributed tracing, performance debugging, and system reliability engineering for cloud-native environments.

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

Set up comprehensive debugging environments, distributed tracing, and diagnostic tools for development and production. Configure tracing, logging standards, and troubleshooting workflows to diagnose multi-service issues efficiently.

This skill covers .NET/C# binary reverse engineering, including managed PE analysis, debugging with dnSpyEx, deobfuscation using de4dot for tools like ConfuserEx, and IL patching. It supports analyzing red-team Sharp* tooling and distinguishing between managed and native binaries.

Provides expertise for analyzing DWARF debug files and understanding the DWARF debug format/standard (v3-v5). Triggers when understanding DWARF information, interacting with DWARF files, answering DWARF-related questions, or working with code that parses DWARF data.

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.