Skill UI

SymPy is a powerful Python library designed for symbolic mathematics. Unlike numerical solvers, it performs exact calculations using mathematical symbols, allowing users to model and solve complex problems across various domains. Core capabilities include symbolic calculus (derivatives, integrals, limits), advanced algebra, solving systems of equations, linear algebra (determinants, eigenvalues), and specialized modules for physics and number theory. It can also generate executable code in languages like C and Fortran, making it invaluable for academic research and scientific computing.

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`.

Automates the entire Continuous Integration (CI) workflow, crucial before opening a Pull Request (PR). It systematically runs local unit tests for multiple languages (Python, Node, Rust), performs type checking, pushes the current branch to the remote repository, and finally provides the direct URL and status check for the CI platform (GitHub Actions/GitLab). This single command replaces multiple manual steps.

This tool provides a unified interface to run test suites for various programming stacks (Python, Node.js, Rust) within a CI/CD pipeline. It automatically detects the project's environment (by checking lock files or config) and executes the appropriate testing framework (e.g., pytest, vitest, cargo test), ensuring consistent code quality checks regardless of the underlying language. Ideal for pre-commit or CI/CD integration.

This skill facilitates the computation of Electronic Clinical Quality Measures (eCQMs) by leveraging CQL (Clinical Quality Language) logic against the QDM (Quality Data Model). It is designed to extract crucial facts—such as counseling provided, symptoms, or documented exclusions—from unstructured clinical notes. By lifting these facts, it supplements structured EHR data, ensuring that numerators and valid exclusions are accurately captured for quality measure evaluation. Note that this tool augments data capture and does not replace certified measure engines.

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 skill de-identifies sensitive PII from clinical notes across numerous languages, including Spanish, German, French, and Arabic. It automatically selects language-specific PII models, national ID regex patterns, and generates locale-aware fake surrogates. Designed for global healthcare datasets, it runs entirely on-device, ensuring maximum privacy and linguistic accuracy for diverse international clinical records.

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.

The Diagram Generator converts complex, unstructured inputs—such as natural language, code snippets, schemas, and notes—into professional, editable visual diagrams. It supports diverse standards including Mermaid, PlantUML, and Graphviz, enabling the creation of flowcharts, sequence diagrams, UML models, ER diagrams, architecture maps, and more. Outputs are designed to be reviewable, version-controllable, and highly precise.

Utilizes OpenMed's analyze_text to perform sophisticated Named-Entity Recognition (NER) on clinical and biomedical text. This tool extracts structured entities such as diseases, drugs, anatomy, genes, and proteins. It provides precise character spans and model confidence scores, making it essential for building robust, privacy-first clinical Natural Language Processing (NLP) pipelines.

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.

A comprehensive framework for conducting authorized security assessments on Large Language Models (LLMs) and AI agents. It covers critical vulnerabilities including advanced prompt injection (OWASP Top 10), tool abuse, RAG data poisoning, memory manipulation, and system prompt extraction. Ideal for red teaming, security auditing, and ensuring the robustness of AI systems in production environments.