Skill UI

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

Systematically turn threat intelligence, attack patterns, and telemetry into testable queries so SOC teams can proactively hunt, validate, and report on attacks during threat assessments, purple team exercises, or incident response.

Structured threat-hunting workflow that detects anomalous service-account logons, lateral movement, and privilege escalation by guiding analysts through hypothesis formation, SIEM/EDR querying, correlation and validation for proactive response.

Proactively hunt for Advanced Persistent Threats (APTs) by building hypothesis-driven searches across telemetry, network logs, and memory artifacts, using MITRE ATT&CK, Velociraptor, osquery, Zeek, and Sigma-compatible playbooks to validate or rule out known TTPs.

This end-to-end workflow automates the transition from a vague research direction to a structured, actionable proposal. It first refines the core method thesis and dominant contribution (using research-refine), and then automatically generates a detailed, claim-driven experiment roadmap and comprehensive pipeline summary. Ideal for researchers needing to transform initial concepts into a complete, executable paper proposal and experimental plan.

Hard screening skill runs deterministic VC analyses where Claude gathers context, Python scores/formats, and the pipeline produces auditable verdicts with investment thesis guidance.

Generates consulting-grade research reports in markdown, guiding users through framework creation and final synthesis across market, finance, brand, and industry domains with strict data traceability.

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.

Performs a systematic and structured investigation to determine the true source of bugs, errors, and unexpected system behavior, rather than merely treating symptoms. This method utilizes techniques like the Five Whys, hypothesis-driven testing, stack trace analysis, and gathering evidence (logs, git diffs) across multiple phases. It is ideal for complex, intermittent, or deeply rooted technical issues in software development.

This expert tool guides users through rigorous statistical analysis, specializing in A/B testing, hypothesis testing, and interpreting experimental results. It helps define sample sizes, executes appropriate tests (Z-test, T-test, Chi-square), and critically distinguishes between 'statistical significance' and 'practical significance' to ensure data-driven decision-making for product and business strategy.

A comprehensive, four-stage methodology for debugging complex software issues, ensuring that fixes target the root cause rather than merely masking symptoms. This guide mandates rigorous investigation (gathering evidence, tracing data flow), pattern analysis, controlled hypothesis testing, and disciplined implementation via test-driven development. Use this when facing production bugs, test failures, performance degradation, or unexplained system behavior, especially under time pressure.

This skill guides the systematic process of identifying and verifying candidate football entities (players or clubs). Use it when initial search results suggest a lead, or when the question provides enough detailed constraints (e.g., nationality, era, league) to hypothesize a candidate. The technique involves constructing targeted name-based searches and cross-checking every constraint (birth year, career timeline, etc.) against the found profile to ensure accuracy.