Skill UI

Hunt for encoded, download cradle, AMSI bypass, and constrained language PowerShell executions by formulating hypotheses, querying SIEM/EDR telemetry, correlating events, and validating findings to guide containment and response.

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.

Hunt for unusual network connections by analyzing outbound traffic patterns, rare destinations, non-standard ports, and anomalous frequencies, using SIEM/EDR telemetry to validate hypotheses, eliminate false positives, and align findings with MITRE techniques.

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 skill teaches users how to optimize search queries for specific, structural information found within academic theses or dissertations. It emphasizes the critical need to include qualifying terms like "acknowledgment," "thesis," or "dissertation" in searches to successfully retrieve details about committee members, dedicating recipients, or other section-specific data, rather than just topic abstracts. This dramatically improves the relevance and accuracy of information retrieval.

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.

This skill teaches the methodology of forming concrete hypotheses about specific entities (such as film titles, director names, or historical events) when presented with constraints. Instead of performing broad, generic searches, users should leverage their world knowledge to narrow down possibilities, turning a wide query into a precise, verifiable search query. This is particularly effective for academic or historical research.

This technique is designed for advanced academic research, enabling users to search for theses by running two parallel tracks: a Topic Track (focusing on discipline, methodology, and materials) and a People Track (focusing on university, advisor, degree type, and year). By switching between these two methodologies, users can cross-validate results and significantly improve the accuracy and completeness of their literature review.

Streamlines citation discovery via Google Scholar and PubMed, extracts complete metadata, validates references, and generates formatted BibTeX for accurate academic manuscripts or theses.