What this resource does
Core uses
This repository is a large collection of separate scientific Skills
The official README currently documents 148 scientific and research Skills. This page groups a representative selection by academic task so you can understand the collection before opening the full catalogue.
The component cards below are representative, not a complete list of all 148 Skills.
Scientific literature and database access
Provides separate Skills for literature work and querying scientific databases in biology, chemistry, medicine, materials science, and related fields.
- Typical inputs
- Research question, entity, identifier, or search terms; Selected database or domain Skill
- Output
- Source records, literature results, or structured scientific metadata from the selected component.
Best for: Finding research evidence or retrieving domain-specific scientific information.
Skills for this task
database-lookup
Queries documented public database APIs with explicit endpoints, filters, pagination, and provenance.
literature-review
Conducts structured literature reviews across academic databases and creates Markdown or PDF review documents.
biopython
Supports sequence processing, biological file parsing, phylogenetics, and programmatic NCBI or PubMed access.
Suggested workflow requests (1)
Use available skills you have access to whenever possible. Parse VCF with pysam, annotate variants with Ensembl VEP, query ClinVar for pathogenicity, check COSMIC for cancer mutations, retrieve gene info from NCBI Gene, analyze protein impact with UniProt, search PubMed for case reports, check ClinPGx for pharmacogenomics, generate clinical report with document processing tools, and find matching trials on ClinicalTrials.gov.
From README.md, Quick Examples.
Computational research analysis
Includes analysis workflows for genomics, chemistry, statistics, machine learning, geospatial science, and scientific simulation.
- Typical inputs
- Research dataset or domain inputs; Selected method and component requirements
- Output
- Component-dependent analysis results, code, figures, or reports.
Best for: Applying a documented scientific package or method to research data.
Skills for this task
scanpy
Runs established single-cell RNA-seq workflows including quality control, clustering, differential expression, and visualisation.
rdkit
Provides fine-grained cheminformatics workflows for molecular parsing, descriptors, fingerprints, similarity, and reactions.
pymc
Supports Bayesian modelling, MCMC, variational inference, model comparison, and posterior checks with PyMC.
geopandas
Works with geospatial vector data for spatial joins, overlays, coordinate transformations, and mapping.
Suggested workflow requests (5)
Use available skills you have access to whenever possible. Query ChEMBL for EGFR inhibitors (IC50 < 50nM), analyze structure-activity relationships with RDKit, generate improved analogs with datamol, perform virtual screening with DiffDock against AlphaFold EGFR structure, search PubMed for resistance mechanisms, check COSMIC for mutations, and create visualizations and a comprehensive report.
Use available skills you have access to whenever possible. Load 10X dataset with Scanpy, perform QC and doublet removal, integrate with Cellxgene Census data, identify cell types using NCBI Gene markers, run differential expression with PyDESeq2, infer gene regulatory networks with Arboreto, enrich pathways via Reactome/KEGG, and identify therapeutic targets with Open Targets.
Use available skills you have access to whenever possible. Analyze RNA-seq with PyDESeq2, process mass spec with pyOpenMS, integrate metabolites from HMDB/Metabolomics Workbench, map proteins to pathways (UniProt/KEGG), find interactions via STRING, correlate omics layers with statsmodels, build predictive model with scikit-learn, and search ClinicalTrials.gov for relevant trials.
Use available skills you have access to whenever possible. Retrieve AlphaFold structures, identify interaction interface with BioPython, search ZINC for allosteric candidates (MW 300-500, logP 2-4), filter with RDKit, dock with DiffDock, rank with DeepChem, check PubChem suppliers, search USPTO patents, and optimize leads with MedChem/molfeat.
Use available skills you have access to whenever possible. Query NCBI Gene for annotations, retrieve sequences from UniProt, identify interactions via STRING, map to Reactome/KEGG pathways, analyze topology with Torch Geometric, reconstruct GRNs with Arboreto, assess druggability with Open Targets, model with PyMC, visualize networks, and search GEO for similar patterns.
From README.md, Quick Examples.
Research communication and laboratory work
Contains Skills for scientific writing, figures, presentations, protocols, electronic laboratory notebooks, and research-platform integrations.
- Typical inputs
- Draft text, figures, protocols, or platform task; Target deliverable and relevant constraints
- Output
- Draft research materials or platform-specific records produced by the selected Skill.
Best for: Preparing research outputs or working with a documented laboratory platform.
Skills for this task
scientific-writing
Drafts scientific manuscripts through an outline-first process with IMRAD structure and reporting-guideline support.
scientific-visualization
Coordinates publication-ready figures with multi-panel layouts, statistical annotations, and journal formatting.
benchling-integration
Connects to Benchling SDK and APIs for registry entities, inventory, ELN entries, workflows, and data queries.
protocolsio-integration
Connects to protocols.io for protocol discovery, editing, collaboration, files, and scientific documentation.
Use boundaries
Limits and checks
Component variability
A collection-level description cannot establish the behaviour of every component.
Inspect and test the selected Skill before research use.
External services
Costs, limits, data transfer, or account terms may apply.
Check the selected component's environment variables and service terms.
Scientific validity
Unchecked output could lead to unsupported research conclusions.
Validate methods, code, calculations, and source records independently.
More boundaries
- The collection does not provide one shared workflow that combines every scientific Skill into a complete research project.
- A component cannot use restricted databases, local software, or credentials that the user has not legitimately provided.
- Scientific interpretations, clinical implications, calculations, and citations require expert review against original evidence.
Technical details
- Resource type
- Skill Collection
- Author or maintainer
- K-Dense
- Version
- v2.53.0
- Latest release
- v2.53.0
- Source last updated
- 8 Jul 2026
- Last verified
- 13 Jul 2026
- Documented applications
- Claude Code, Codex, Cursor, Gemini CLI, OpenClaw, General Agent Skills, Google Antigravity, Claude Cowork, NVIDIA NemoClaw, Hermes, Pi
- Licence
- MIT
- Access
- Publicly available
- Additional costs
- Platform terms or usage limits may apply. API usage fees may apply for selected components. External services, software, compute, or data access may have separate costs.
- Skill instruction language
- English
- Documentation language
- English
- Repository languages
- Python, Markdown
- Dependencies
- Node.js and npx for the recommended installer; Component-specific Python packages, APIs, databases, and credentials
- Review status
- Not tested
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