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.

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 guide covering critical pitfalls and best practices for writing robust PowerShell scripts on Windows. It details essential rules for operator syntax (parentheses), safe path handling, null checking, advanced error management (try/catch), and correct JSON serialization, ensuring your automation code is highly reliable and maintainable.

Guides engineers through a disciplined four-phase debugging workflow so they investigate root causes, analyze patterns, test hypotheses, and implement fixes based on evidence before touching code.

This framework provides a systematic methodology for cybersecurity professionals to transform raw threat intelligence, observed attack patterns, and environmental telemetry into verifiable, testable hunting hypotheses. It guides the end-to-end process, from hypothesis formulation and data identification across SIEM/EDR systems, to deep correlation and reporting, enabling proactive threat detection and scope assessment.

This skill teaches users to apply the scientific method to software debugging. Instead of random changes, it guides the process through five steps: Observation, Hypothesizing, Prediction, Testing, and Analysis. By systematically forming and testing specific, measurable hypotheses, users can efficiently isolate failing components and confirm the true root cause of technical issues.

This advanced technique assists in precisely locating academic theses by combining key identifiers: university name, degree type, and year. Its core strength is resolving indirect university clues (e.g., determining a founding year from a literary work or historical event) first. This two-step resolution process ensures that the final search for the thesis is highly accurate, even when the university is described obliquely.

A systematic, multi-phase guide for tackling hard bugs and performance regressions. It mandates building a robust feedback loop (e.g., failing tests, harnesses), formulating falsifiable hypotheses, targeted instrumentation, and rigorous regression testing. This methodology ensures that debugging efforts are structured, efficient, and highly reproducible, making it ideal for complex software defects.

This skill guides the safe execution of chaos engineering experiments to surface weaknesses in production systems without causing actual outages. It provides tools to design robust experiments by defining hypotheses, calculating the potential 'blast radius' before failure injection, setting strict abort criteria, and generating structured postmortems. Ideal for SRE teams running game days and resilience tests.

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.