Skill UI

This skill guides users in transforming chaotic debugging efforts into a structured, systematic process. It provides a methodology for tracking down elusive bugs, investigating complex performance degradation, and resolving critical production incidents. Users learn to capture necessary logs, formulate testable hypotheses, narrow the scope using techniques like binary search, and methodically verify fixes in distributed systems environments.

Guides developers through reproducing issues, isolating failure cases, testing hypotheses, and verifying fixes with regression safeguards, while referencing tools, logs, stack traces, and git bisect workflows to expose stubborn root causes.

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.

Hunt for adversaries creating malicious email forwarding rules to persist in environments, using SIEM/EDR telemetry to validate hypotheses, analyze anomalies, and document response guidance.

Hunt for registry-based persistence artifacts such as Run keys, Winlogon helpers, IFEO injectors, and COM hijacks by correlating EDR, SIEM, and Sysmon data to validate hypotheses and document findings.

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.