Skills from K-Dense’s repositories that passed our trust, licence and security gates. Each keeps its author’s attribution.
| Repository | Tier | Verdict | Authorship | Checked | Skills |
|---|---|---|---|---|---|
| K-Dense-AI/claude-scientific-writer | Project | Approved · created 2025-10-27, pushed 2026-11-07, 2559 stars | Written in-house | 2026-11-07 | 26 |
| K-Dense-AI/scientific-agent-skills | Project | Approved · created 2025-10-19, pushed 2026-11-02, 56393 stars | Written in-house | 2026-11-07 | 177 |
Files are mirrored only where the licence allows redistribution; otherwise we link to the source.
K-Dense· Community
A structured framework for formulating testable scientific hypotheses and research plans from observations.
K-Dense· Community
A unified interface for querying over 20 bioinformatics databases and analysis tools via CLI or Python.
K-Dense· Community
A workflow for molecular property prediction and MoleculeNet benchmarks using DeepChem 2.8.0.
K-Dense· Community
A comprehensive toolkit for computational mass spectrometry, supporting proteomics and metabolomics data processing and analysis.
K-Dense· Community
A comprehensive toolkit for end-to-end single-cell RNA-seq analysis using Scanpy.
K-Dense· Community
Perform RNA velocity analysis on single-cell RNA sequencing data to infer directional trajectory hypotheses.
K-Dense· Community
Generates transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Used for predicted log1p-CPM expression from 224x224 tiles at about 20x, querying the released protein-coding gene panel by symbol, and whole-slide prediction after resolution-aware tiling with histolab.
K-Dense· Community
Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, protein representation learning, and property prediction.
K-Dense· Community
Google's open-source framework for designing, simulating, and executing quantum circuits on quantum hardware and simulators.
K-Dense· Community
Python interface for HTSlib to read, query, and write genomic file formats like SAM, BAM, CRAM, and VCF.
K-Dense· Community
A secure, manifest-driven workflow for creating and auditing editable scientific posters in PowerPoint.
K-Dense· Community
Analyze your Screenpipe history to automatically detect, draft, and propose new agent skills based on your repeated research workflows.
K-Dense· Community
Integrates the Biohub esm Python SDK for protein generation, ESMC embeddings, and ESMFold2 structure prediction.
K-Dense· Community
Generates scientific Markdown documentation and Mermaid structural diagrams for workflows, schemas, and relationships.
K-Dense· Community
A toolkit for designing, reviewing, and planning MATLAB and GNU Octave numerical workflows.
K-Dense· Community
A high-performance DataFrame library for efficient data manipulation, ETL, and analytics in Python.
K-Dense· Community
Detects and reports available system resources for resource-aware workload planning without performing intrusive benchmarks.
K-Dense· Community
Perform and analyze molecular dynamics simulations for structural biology and biophysics using OpenMM and MDAnalysis.
K-Dense· Community
Perform Particle Image Velocimetry (PIV) analysis to extract and visualize fluid velocity fields from image pairs.
K-Dense· Community
Perform constraint-based metabolic modeling and systems biology analysis using COBRApy.
K-Dense· Community
A Python library for molecular filtering and compound triage in drug discovery.
K-Dense· Community
A serverless cloud platform for deploying and scaling Python applications, AI models, and GPU-accelerated workloads.
K-Dense· Community
Builds and audits reproducible physiological time-series research workflows using the NeuroKit2 library.
K-Dense· Community
Tracks physical units and propagates measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significant-figure and plus-minus reporting, error propagation through curve fits, CODATA constants, auditing Python code for stripped units or broken uncertainty propagation, and order-of-magnitude plausibility checks using dimensionless groups (Reynolds, Peclet, Damkohler, Knudsen, Biot, Womersley), characteristic scales such as diffusion time or Debye length, and observed magnitude ranges. Trigger on "is this number physically reasonable", "sanity check these units", "what regime is this flow in", or a result that looks off by orders of magnitude.