タグ：scientific-computing

166 research skills maintained by K-Dense (formerly Claude Scientific Skills), spanning bioinformatics, cheminformatics, clinical research, materials and physics, including unified access to 78 public databases such as PubChem and UniProt. Works with Claude Code, Codex and Cursor.

Unified database lookup skill from K-Dense's scientific collection: reproducibly queries about 80 public database APIs such as PubChem, UniProt, ChEMBL, ClinicalTrials.gov and FRED, returning traceable results with endpoints, parameters and count checks. Some need API keys.

Teaches agents standard single-cell RNA-seq analysis with Scanpy: QC, normalization, PCA/UMAP, clustering, differential expression and plots, with ready-to-run pipeline scripts and guidance on converting Seurat and other R objects to h5ad. Needs Python 3.12+; by K-Dense.

Guides agents to the right statistical method for research data: t-tests, ANOVA, chi-square, regression and Bayesian analysis, checking assumptions, computing effect sizes and power, and reporting results in APA format. Python-based (pingouin, statsmodels); by K-Dense.

Official Anthropic plugin that solves competition math such as IMO and Putnam problems, with adversarial checks by an independent verifier that only sees the clean proof. Gives up explicitly rather than bluffing when unsure, and outputs LaTeX/PDF once verified.

Creates, edits and validates reproducible Python and SQL Jupyter notebooks: organizes cells as reports or tutorials, checks conclusions against actual output and reruns top to bottom before handoff. Maintained by OpenAI in the Data Analytics plugin of openai/plugins.

OpenAI's official Codex plugin that guides sequencing analysis from BCL/FASTQ: QC, RNA-seq differential expression, single-cell, variant calling and metagenomics, favoring nf-core and other public pipelines with reproducible run logs. Checks for local tools first. Research use only.

Converts and two-way syncs Jupyter notebooks with plain Python scripts or Markdown, so agents can edit research and teaching notebooks like ordinary text and diffs stay readable. Needs a local Python environment; install with pip.

Empirical social science skills from Stanford REAP and CoPaper.AI, bundling 77 in-house and third-party collections: topic selection, literature reviews, DID/RD/IV identification, regression tables in Python, Stata or R, robustness audits and paper writing. Stata flows need Stata installed.

Builds Markdown and Jupyter notebooks into online textbook sites with a TOC, math, citations and cross-references, plus publication-quality PDF export; popular for course notes. A Jupyter subproject installed via pipx or npm; PDF export needs Typst or LaTeX.

NVIDIA's official plugin that finds and installs the right skills from NVIDIA's catalog for your task, spanning CUDA acceleration, NIM inference, NeMo training, RAG blueprints, Omniverse simulation and robotics. Listed in both the Claude and Codex official marketplaces.

NVIDIA's official, verified catalog of hundreds of skills covering CUDA-X libraries, NeMo training, NIM and Dynamo inference, RAG blueprints, Jetson, Omniverse and robotics simulation. The install adds only nvidia-skill-finder, which recommends other skills and installs them once you confirm.

About 40 research skills from Google DeepMind: search arXiv, PubMed and OpenAlex, query UniProt, PDB, ClinVar, ChEMBL and more, run AlphaGenome variant analysis and render structures in PyMOL. Requires uv; AlphaGenome needs an API key, and keys raise limits for OpenAlex, ClinVar and others.

A three-stage workflow for math modeling contests like MCM/ICM and CUMCM: analysis, solving and plotting in Python or MATLAB, and a Word (optionally LaTeX) paper, with stage-check subagents and OpenAlex plus AnySearch literature search. Generated papers are for reference only.

From Harvard Medical School's Zitnik Lab: one MCP connecting 1,000+ scientific tools and databases for drugs and targets, genes and proteins, clinical trials, FDA data and PubMed, for biomedical and drug research. Runs with uv; some sources need API keys; medical conclusions need expert review.

MathWorks' official server that lets agents start your local MATLAB, write and run scripts and unit tests, and check code style and potential errors, for computing, simulation and data analysis. Needs a licensed MATLAB R2021a+ and the downloaded server binary.

Turns secondary-school math and science problems into teaching materials: interactive geometry solution pages, 3D molecular views of chemical reactions, and math explainer videos with Chinese voiceover and bilingual subtitles (needs a Zhipu GLM-TTS key). Requires Python sympy.

Connects agents to live Jupyter notebooks: read, write, insert and execute cells and read multimodal outputs like charts, for interactive data analysis. Bring your own JupyterLab or JupyterHub; open source, maintained by Datalayer.

MathWorks' official toolkit for reading, building, modifying and testing Simulink models via the MATLAB MCP server, with skills for control design, Simscape, code generation and compliance checks. Needs licensed MATLAB R2023a+ and Simulink; run the official installer in MATLAB first.

Breaks a rough explanation idea into a scene-by-scene storyboard, then writes animation code following best practices for Manim Community or 3Blue1Brown's ManimGL, for math and physics explainers in class demos and educational videos. Needs Python, FFmpeg and LaTeX installed locally.

MathWorks' official toolkit: set up the MCP server via the MATLAB installer so agents can run code, run tests and do static checks; domain skill sets for signal processing, controls, statistics and machine learning install as Claude Code plugins. Needs MATLAB R2021a+.

100+ tools that let agents drive COMSOL Multiphysics: build models, set geometry and physics (heat transfer, fluids, electrostatics, solid mechanics), mesh, solve and export results; the accompanying paper is in Neurocomputing. Needs licensed COMSOL 5.x/6.x, Python and Java; install from the repo.

MCP server for the Lean 4 theorem prover: lets agents read proof goals, diagnostics and definitions and search theorems via LeanSearch and Loogle, helping mathematicians and students with formal proofs. Needs uv and the Lean toolchain; run it from the Lean project root.

Connects Stata to VS Code or Cursor and exposes a local MCP endpoint so Claude Code or Codex can write and run do-files and read regression output, with graphs shown in the editor; for empirical research. Needs a licensed Stata 17+, the extension installed and the editor open.