Skill UI

gget is a versatile CLI and Python package designed for rapid, reproducible bioinformatics searches across major genomic reference databases (e.g., Ensembl, UniProt). It allows users to quickly query gene metadata, perform initial BLAST/BLAT style alignments, and explore associated pathways, cancer, or disease modules. It is ideal for initial data exploration and generating reproducible evidence logs before building complex, full-scale local pipelines.

Build a local multimedia webpage project from a user topic, audience, language, style, and media preferences by framing requirements, researching, planning, acquiring media, generating files, packaging, validating, repairing, and delivering usage guidance.

This skill helps developers programmatically discover, filter, and inspect the entire OpenMed model registry. It allows users to find the correct model key for specific clinical tasks (e.g., diseases, oncology, PII detection), filter by language, size, or task, and review model metadata before loading. It pairs with `loading-openmed-models`.

A comprehensive, authorized methodology for security assessments covering cloud platforms, container runtimes, and Kubernetes clusters. It guides users through checking critical areas such as IAM privilege escalation, metadata SSRF, container escape paths, and granular RBAC misconfigurations, ensuring testing is systematic and non-destructive.

This specialized skill is designed for advanced security analysis, tracing the complete trust chain from cloud metadata services to accepted privilege. It is used in penetration testing or CTF environments to meticulously prove how metadata-derived credentials (such as instance identities or tokens) are issued and subsequently accepted by a cloud or control plane API, thereby exposing the decisive trust edge.

This specialized downstream skill is designed for deep analysis of complex attack paths originating from Server-Side Request Forgery (SSRF). It systematically tracks the entire exploitation chain: from the initial SSRF source (e.g., webhook, URL parameter) through internal service reachability, metadata endpoint exploitation, credential extraction, and finally, demonstrating how an attacker achieves accepted access or elevated privileges. Use this when tracing the decisive path through server-side requests and credential pivoting.

This advanced skill specializes in detecting and recovering hidden data (steganography) embedded within various media containers, including images, audio, video, and documents. It is crucial for forensic analysis and CTF challenges, allowing users to inspect metadata, LSBs, alpha/palette channels, and appended trailers to reconstruct hidden payloads without brute force.

This skill extracts critical patient metadata (PHI) from DICOM headers using pydicom, and recursively pulls structured report narrative text from DICOM-SR content trees. It is essential for ingesting medical images into NLP pipelines, enabling subsequent de-identification and clinical text analysis.

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 resolves entities extracted by OpenMed (e.g., Disease, Drug) to standardized Concept Unique Identifiers (CUIs) within the UMLS Metathesaurus. It is essential for cross-vocabulary normalization, allowing users to unify concepts (like SNOMED CT, ICD-10, RxNorm) to a single canonical CUI. Crucially, the process relies entirely on the user's own UTS API key, ensuring that the Metathesaurus content is never bundled or cached.

This skill programmatically searches and fetches biomedical literature from PubMed and PMC using NCBI E-utilities (ESearch, EFetch, ESummary). It allows users to gather large corpora of abstracts, full texts, and metadata related to specific conditions, drugs, or genes. Ideal for building evidence bases, performing biomedical Named Entity Recognition (NER), or summarizing scientific findings.

An adapter for generating short, browser-playable video assets using OpenRouter's asynchronous video generation API. This skill is designed for meta-skills to integrate dynamic multimedia content into web pages. It handles the full job lifecycle—submitting the request, polling for completion, and downloading the final MP4 asset—and adheres strictly to the required manifest reporting format.