Skill UI

A scientific, customer-centric framework for systematically improving conversion rates on websites and landing pages. This methodology guides users through funnel mapping, identifying core visitor objections, and conducting evidence-based A/B testing. It is essential for auditing underperforming funnels, optimizing checkout flows, and transforming general marketing hypotheses into measurable, statistically significant improvements.

This skill provides a systematic framework for analyzing cyber campaign attribution. It involves collecting, weighting, and correlating diverse indicators—such as infrastructure overlaps, TTP consistency, malware code similarities, and operational patterns—to determine the responsible threat actor. Utilizing structured methodologies like Analysis of Competing Hypotheses (ACH) and the Diamond Model, users can build confidence-weighted assessments to enhance threat intelligence accuracy.

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 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.

Hypogenic is an advanced framework that uses Large Language Models (LLMs) to systematically generate and test hypotheses on complex tabular datasets. It supports three powerful modes: data-driven generation (HypoGeniC), literature-grounded refinement (HypoRefine), and combined union methods. This tool is essential for accelerating scientific discovery in fields such as predictive modeling, content analysis, and deception detection by combining empirical data insights with theoretical knowledge.

A systematic tool for scientific inquiry, guiding users through the entire research process. It helps formulate testable, mechanistic hypotheses from raw observations or preliminary data, following the established scientific method. The workflow includes comprehensive literature review, evidence synthesis, generating competing explanations, and designing experiments, all while mandating the creation of scientific schematics for visualization.

This skill acts as a collaborative research ideation partner, guiding users through a structured brainstorming process. It helps generate novel hypotheses, explore interdisciplinary connections, challenge underlying assumptions, and identify potential research gaps. Use it when starting a project without specific observations, or when overcoming creative blocks in scientific problem-solving.

This skill specializes in generating visual representations of complex logic flows, particularly within finance. It creates Draw.io XML compatible diagrams, making it ideal for visualizing investment theses, signal transmission chains, or complex financial models for clear communication and analysis.