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sci-data-analysis-viz

Name: sci-data-analysis-viz
Author: galeep

By galeep

Stats, EDA, network analysis, survival analysis, plotting, big-data dataframes (dask/polars/vaex/zarr), MATLAB, and US fiscal data.

npx claudepluginhub galeep/plugin-place --plugin sci-data-analysis-viz

Popularity

Stars

Med: 0·Avg: 281

Installs

Med: 0·Avg: 1

What's Inside

Skills14

dask

/dask

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

exploratory-data-analysis

/exploratory-data-analysis

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

matlab

/matlab

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.

matplotlib

/matplotlib

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.

networkx

/networkx

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

Stats

Version0.1.0

LanguagePython

Stars0

MaintenanceGood

LicenseMIT

Last CommitMay 17, 2026

AddedMay 17, 2026

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View on GitHub View README Plugin Marketplace JSON

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Available In

plugin-place

README

plugin-place

A curated Claude Code plugin marketplace. Currently hosts K-Dense AI's scientific tooling split into focused, individually-installable plugins so you only load what you need.

Quick start

# Add the marketplace (one time)
claude plugin marketplace add galeep/plugin-place

# List what's available
claude plugin search @plugin-place

# Install whichever pieces you want
claude plugin install sci-bioinformatics-genomics@plugin-place
claude plugin install sci-machine-learning@plugin-place
claude plugin install kdense-document-skills@plugin-place

What's in here

19 plugins, 135 skills, all sourced from K-Dense AI and licensed MIT. Two upstream repos vendor in, pinned to release tags:

K-Dense-AI/scientific-agent-skills @ v2.38.0 — 135 scientific skills, split here into 17 domain plugins plus the general-purpose kdense-document-skills plugin
K-Dense-AI/claude-scientific-writer @ v2.13.0 — full writer plugin with a /scientific-writer-init command

The plugins

Plugin	Skills	What it covers
`sci-bioinformatics-genomics`	20	Sequence analysis, scRNA-seq, gene regulatory networks, variants, phylogenetics, biomedical DBs
`sci-cheminformatics-drug-discovery`	9	Cheminformatics, molecular ML, docking, medicinal chemistry
`sci-proteomics-mass-spec`	3	LC-MS/MS, spectral matching, glycoengineering
`sci-clinical-research`	4	CDS, clinical/case/trial reports, treatment plans, ISO 13485
`sci-healthcare-ai`	2	PyHealth, NeuroKit2 biosignal processing
`sci-medical-imaging`	4	DICOM, WSI, computational pathology, NCI Imaging Data Commons
`sci-machine-learning`	16	scikit-learn, Lightning, transformers, RL, time series, GNNs, Bayesian, SHAP, GPU/compute helpers
`sci-materials-chemistry`	2	pymatgen, COBRApy
`sci-physics-astronomy`	6	astropy, sympy, qutip, qiskit, cirq, pennylane
`sci-engineering-simulation`	4	SimPy, pymoo, CFD, molecular dynamics
`sci-data-analysis-viz`	14	Stats, EDA, networks, survival, plotting, big-data dataframes, MATLAB, US fiscal data
`sci-geospatial`	2	GIS, remote sensing, earth-observation ML
`sci-lab-automation`	11	Benchling, DNAnexus, LatchBio, OMERO, Opentrons, protocols.io, PyLabRobot, flow cytometry, Neuropixels
`sci-scientific-communication`	22	Lit review, peer review, writing, citations, posters, slides, schematics, infographics, academic web search
`sci-multi-omics`	3	DepMap, PrimeKG, scvi-tools
`sci-protein-engineering`	2	ESM, Adaptyv Bio Foundry
`sci-research-methodology`	7	Hypothesis generation, grant writing, brainstorming, critical thinking, scenario analysis
`kdense-document-skills`	4	General-purpose .docx, .pdf, .pptx, .xlsx tools (useful with any plugin)
`claude-scientific-writer`	23	K-Dense's full writer plugin including the `/scientific-writer-init` command

Overlap warning: writer vs. sci-* plugins

The claude-scientific-writer plugin and the sci-* plugins share most of their skills (K-Dense maintains the same skill code in both upstreams). Specifically:

23 of the writer's 23 skills also appear in one of the sci-* plugins (mostly sci-scientific-communication, plus sci-clinical-research and sci-research-methodology)
The writer's unique contribution is the /scientific-writer-init slash command (which the bare skills lack)

Pick one approach:

Install the writer: get all the writing/clinical skills plus the init command, in a single plugin
Install individual sci-* plugins: get exactly the domain slices you want, granular enable/disable

Installing both will give you duplicate skill names, which is unsupported and will confuse Claude Code's skill router.

How this is built

plugins.yaml is the source of truth. Everything else is generated:

git submodule update --init --recursive
bash scripts/build.sh

This regenerates plugins/* and .claude-plugin/marketplace.json from the YAML and the pinned upstream submodules. The build is idempotent — edits inside plugins/* will be overwritten.

The built plugin kind copies a chosen subset of skills from an upstream submodule. The vendored plugin kind copies an entire upstream plugin intact (skills, commands, agents, hooks) and generates a plugin.json from its upstream marketplace metadata. A local kind is reserved for plugins authored directly in this repo.

To add a new plugin, edit plugins.yaml and rerun scripts/build.sh.

Staying in sync with upstream

View full README on GitHub

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sci-data-analysis-viz

Popularity

What's Inside

Confidence

README

plugin-place

Quick start

What's in here

The plugins

Overlap warning: writer vs. sci-* plugins

How this is built

Staying in sync with upstream

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Quick start

What's in here

The plugins

Overlap warning: writer vs. sci-* plugins

How this is built

Staying in sync with upstream

Popularity

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sci-bioinformatics-genomics

sci-cheminformatics-drug-discovery

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