Skill UI

This advanced skill specializes in tracing the complete provenance of software artifacts across the entire development lifecycle. It is designed for deep security analysis of CI/CD pipelines, identifying dependency drift, build tampering, package origin, and determining the final artifact consumed at runtime. Use it when investigating integrity vulnerabilities in complex release flows.

A specialized downstream skill for deep analysis of complex web rendering pipelines. Use this to trace vulnerabilities or logical flaws residing in Server-Side Rendering (SSR), template data, route loaders, or client-server hydration boundaries. It compares template-level gating against backend handler enforcement to pinpoint the decisive weak point in the render chain.

This specialized skill analyzes the complete lifecycle of real-time web communications, including WebSocket and Server-Sent Events (SSE). It is designed to map the initial handshake, decode complex message streams (subscriptions, keepalives, pushes, reconnects), and trace how frame activity leads to persistent or rendered state changes. Use this to isolate the minimal, decisive sequence of events that prove a system's behavior.

A specialized skill for analyzing complex, host-to-host lateral movement chains within a Windows environment. It focuses on tracing protocols like WinRM, SMB, and RDP, replaying captured Kerberos tickets, understanding privilege edges, and identifying the exact accepting service or host that facilitates credential movement. Used in advanced penetration testing and digital forensics scenarios.

This skill allows users to select and implement various compliance-aligned privacy policies (such as HIPAA Safe Harbor, GDPR pseudonymization, and PIPEDA) for robust de-identification. It manages complex policy profiles, ensuring data is masked, redacted, or pseudonymized according to specific legal requirements, while also supporting custom surrogate generators for advanced research needs.

This is the master workflow and default entrypoint for complex CTF, exploit, and digital forensics challenges. It establishes a 'sandbox model,' treating presented targets as internal and guiding the user through a systematic investigation process. The orchestrator maps the entry surface, proves minimal attack paths, and intelligently routes the investigation to the most relevant specialized domain (e.g., pwn, crypto, cloud, AD) while maintaining a cohesive analysis flow.

This skill guides the creation of computable phenotypes and patient cohorts using the OHDSI/OMOP CDM standards. It allows users to define precise, executable criteria by combining standard concept sets (like PheKB) with advanced Natural Language Processing (NLP) features extracted from free clinical text. Use this when structured codes alone are insufficient to capture critical health information (e.g., symptoms, social context).

This tool de-identifies sensitive free-text columns (e.g., names, dates, identifiers) in local structured datasets (CSV, JSONL, Parquet) using advanced OpenMed models. It ensures strict privacy by generating a completely redacted dataset and a separate PHI-free aggregate summary, allowing for secure data sharing and analysis without compromising source privacy or logging sensitive values.

A powerful, on-device tool for removing, masking, or replacing sensitive Protected Health Information (PHI/PII) from clinical free text. It ensures patient privacy by running fully locally, supporting methods like masking, hashing, and date shifting. Essential for preparing medical records for safe sharing, storage, or analysis, adhering to standards like HIPAA and GDPR.

This local developer tool inspects dependency manifests (requirements.txt, pyproject.toml, package.json) for surface-level issues like standard-library shadowing, unpinned dependencies, and conflicts. It provides plain-language explanations and suggested fixes, helping developers debug dependency rot before deeper installation attempts.

This skill computes disproportionality metrics (PRR, ROR, EBGM, IC) using FAERS/OpenFDA drug-event data. It analyzes spontaneous reports to statistically screen for potential drug-reaction associations (Signal of Disproportionate Reporting, SDR). By constructing a 2x2 contingency table, it helps pharmacovigilance professionals identify over-reported adverse events, providing a statistical hypothesis for drug safety assessment.

Generates high-quality, structured technical documentation for READMEs, APIs, and architecture guides, adhering to progressive disclosure principles. It also specializes in creating formal, evidence-backed security reports for penetration testing, CTF writeups, and reverse engineering tasks, ensuring a professional, reusable format.