Skill UI

This specialized skill provides a deep workflow for reverse engineering stripped Go and Rust binaries. It guides the recovery of lost runtime metadata, including symbols, pclntab data, and panic strings. The process addresses language-specific challenges, such as Go's interface structures and Rust's complex async state machines, serving as a crucial complement to general decompilers like IDA or Ghidra.

This skill implements an advanced reference pattern for integrating with Monitoring Control Platform (MCP) servers. It ensures accurate data retrieval by first reading a domain-specific 'gotchas' reference file, mitigating query failures caused by complex server behaviors, pagination limits, or subtle API quirks. It analyzes issue data, groups errors by root cause, and generates a structured, actionable markdown report for improved system health visibility.

A powerful, dependency-free CLI tool designed for programmatically creating, reading, analyzing, and modifying Microsoft Office files (.docx, .xlsx, .pptx). It supports deep DOM editing (L2) and raw XML manipulation (L3), allowing users to extract structured data, validate schemas, and perform complex document automation tasks from a single binary.

This skill parses standardized C-CDA/CCD XML clinical documents exported from EHRs. It extracts both human-readable section narrative text and coded entries, mapping them by LOINC codes and template IDs. It is essential for preparing clinical free-text data for subsequent NLP analysis and supports XML-aware de-identification while preserving document structure.

This skill processes clinical text to interpret laboratory results. It parses reference ranges (e.g., "135-145", "<5") and derives structured abnormal flags (low, normal, high, critical) for a given numeric value. It is unit-agnostic and respects explicit flags provided by the originating lab, making it crucial for structured clinical data extraction and interpretation workflows.

This skill parses complex, free-text clinical trial eligibility criteria from sources like ClinicalTrials.gov into structured, machine-readable rules. It then evaluates these criteria against a comprehensive patient profile (derived from OpenMed data) to determine potential eligibility. The output provides an explainable verdict (eligible, ineligible, or unknown) for every criterion, serving as a critical decision support tool for clinicians and researchers.

This skill connects extracted drug entities with authoritative regulatory facts from the public OpenFDA API. It retrieves Structured Product Labeling (SPL) data, NDC package codes, approved indications, boxed warnings, and current recall/enforcement status. It is essential for enriching drug profiles, providing regulatory context, and supporting unexpectedness checks. Note: This skill provides information, not clinical decision support.

Streamlines the complex process of documenting software releases. This tool scans Git history, categorizes changes (features, fixes, performance), and generates multiple outputs—including a structured CHANGELOG, PR body, and user-friendly communication announcements—while also detecting necessary database migrations. Ideal for professional software teams preparing for production deployments.

This skill converts free-text radiology narratives (CT, MRI, X-ray, etc.) into structured, machine-readable data. It automatically identifies and segments key report sections (Technique, Findings, Impression) and extracts critical clinical details such as anatomy, measurements, laterality, BI-RADS/Lung-RADS scores, and follow-up recommendations. It is crucial for building structured medical records and decision support systems.

This skill guides the researcher through the comprehensive process of updating the AI agent security threat intelligence database. It involves executing targeted searches for new malicious skills, CVEs, attack vectors, and defensive tools. The process mandates critical analysis, de-duplication, risk assessment, and structured version control to maintain a cutting-edge security posture for AI agents.

This guide outlines a structured methodology for authorized wireless security assessments. It covers essential techniques such as Wi-Fi data capture, WPA/WPA2 handshake analysis, and rogue Access Point detection. It is designed for assessing corporate network security, verifying wireless isolation, and providing professional penetration testing recommendations, strictly adhering to legal scope and authorization.

This utility is designed to safely read and display the contents of various configuration files. It is essential for inspecting settings, verifying file structure, and debugging without the risk of modifying any data. Simply provide the file path to view its contents.