Skill UI

A comprehensive guide for researchers submitting to major speech conferences (like INTERSPEECH). It details how to harden the reproducibility of speech results, covering crucial 'decay channels' such as corpus versions, text normalization rules (WER), objective metrics (EER), subjective protocols (MOS), and model stochasticity. The guide emphasizes pinning specific toolkits, commit hashes, configurations, and hardware details to ensure scientific rigor and verifiability.

Scanpy is a powerful, scalable Python toolkit built on AnnData for comprehensive single-cell RNA-seq data analysis. It supports the complete biological workflow, including quality control, normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker gene identification, and sophisticated cell type annotation, enabling the generation of publication-quality figures.

This skill constructs a comprehensive, chronologically sorted patient timeline from raw clinical entities extracted from medical notes. It specializes in temporal normalization, resolving both absolute and relative dates (e.g., 'two days after admission') against a defined anchor date. The resulting sorted sequence of diagnoses, medications, and procedures is ready for immediate visualization or seamless export to FHIR resources.

This advanced skill is designed for security research and CTF environments to trace subtle divergences in request handling across multiple network hops (proxies, gateways, backend servers). It focuses on identifying vulnerabilities stemming from differential interpretation of headers, path normalization, transfer framing (CL/TE), and host derivation, crucial for detecting request smuggling and parsing inconsistencies.

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.

Guides full contract lifecycle review from intake through playbook normalization, clause review, deviation scoring, negotiation planning, approvals, QA, and next actions. Covers legal, business, operational, compliance, privacy, security, technology, and AI risks across NDAs, SaaS agreements, DPAs, procurement and commercial contracts.

This skill applies professional mastering techniques to an existing musical production. It performs loudness normalization (defaulting to -14 LUFS, suitable for streaming platforms) and peak limiting, ensuring the track sounds balanced, robust, and ready for radio or major streaming services. Use this when the core music is ready but requires final professional polish.

This tool automates the cleanup and standardization of complex legal documents written in French (Word format). It distinguishes between deterministic corrections (typography, spacing, obvious errors) applied automatically, and context-sensitive revisions (judgment rewrites) that require intelligent human review. It generates a detailed, reversible registry of all changes, ensuring high accuracy for professional legal writing.

This is an internal, deterministic consent gate used in the meta-short-drama workflow. It normalizes user-provided draft review comments and post-revision confirmation replies. Its primary function is to enforce a secure, bounded decision process, requiring explicit approval at every stage before any external image or video generation is authorized, thus preventing ambiguous inputs from proceeding.

This skill queries the NCATS Translator ARAX API for biomedical knowledge-graph relationships. It supports one-hop and two-hop lookups with Biolink constraints, entity normalization, and provider federation. Users can inspect provenance and edge bindings. It is designed for public research queries only, requiring Python 3.10+ and HTTPS access. Do not use for clinical guidance or sensitive data.

This comprehensive guide covers best practices for building robust database systems. It teaches crucial concepts like schema normalization, effective indexing strategies, selecting the right database (e.g., SQLite vs PostgreSQL) and ORM, and optimizing queries to prevent common performance issues like N+1 problems. It emphasizes making context-aware architectural decisions.

Everyday TypeScript data transformations using functional patterns: array reshaping, object normalization, grouping, aggregation, and null-safe nested access with practical examples.