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Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 148 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.
What's inside
.github/
workflows/
pr-skill-scan.yml
release.yml
security-scan.yml
.gitignore
CONTRIBUTING.md
docs/
examples.md
k-dense-web.gif
open-source-sponsors.md
skills.md
LICENSE.md
pyproject.toml
README.md
scan_pr_skills.py
scan_skills.py
SECURITY.md
skills/
adaptyv/
references/
api-endpoints.md
SKILL.md
aeon/
references/
anomaly_detection.md
classification.md
clustering.md
datasets_benchmarking.md
distances.md
forecasting.md
networks.md
regression.md
segmentation.md
similarity_search.md
transformations.md
SKILL.md
anndata/
references/
best_practices.md
concatenation.md
data_structure.md
io_operations.md
manipulation.md
SKILL.md
arbor/
references/
arbor-upstream.md
executor-brief.md
htr-methodology.md
report-template.md
scripts/
tree.py
SKILL.md
arboreto/
references/
algorithms.md
basic_inference.md
distributed_computing.md
scripts/
basic_grn_inference.py
SKILL.md
astropy/
references/
coordinates.md
cosmology.md
fits.md
tables.md
time.md
units.md
wcs_and_other_modules.md
SKILL.md
autoskill/
.gitignore
config.yaml
references/
https-proxy.md
screenpipe-config.yaml
scripts/
autoskill.py
backends.py
cluster.py
doctor.py
fetch_window.py
match_skills.py
promote.py
redact.py
run.py
synthesize.py
SKILL.md
tests/
conftest.py
smoke_lmstudio.py
test_backends.py
test_cli.py
test_cluster.py
test_doctor.py
test_e2e.py
test_fetch_window.py
test_match_skills.py
test_promote.py
test_redact.py
test_run.py
test_synthesize.py
benchling-integration/
references/
api_endpoints.md
authentication.md
eventbridge.md
sdk_reference.md
SKILL.md
bgpt-paper-search/
SKILL.md
bids/
references/
beps.yml
bids_schema.json
bids_specification.md
conversion_tools.md
metadata_fields.md
scripts/
update_schema.py
SKILL.md
biopython/
references/
advanced.md
alignment.md
blast.md
databases.md
phylogenetics.md
sequence_io.md
structure.md
SKILL.md
bioservices/
references/
identifier_mapping.md
services_reference.md
workflow_patterns.md
scripts/
batch_id_converter.py
compound_cross_reference.py
pathway_analysis.py
protein_analysis_workflow.py
SKILL.md
bulk-rnaseq/
references/
counts-and-handoff.md
design-and-qc.md
upstream-manual.md
upstream-nfcore.md
scripts/
build_counts_matrix.py
validate_samplesheet.py
SKILL.md
cellxgene-census/
references/
census_schema.md
common_patterns.md
SKILL.md
cirq/
references/
building.md
experiments.md
hardware.md
noise.md
simulation.md
transformation.md
SKILL.md
citation-management/
assets/
bibtex_template.bib
citation_checklist.md
references/
bibtex_formatting.md
citation_validation.md
google_scholar_search.md
metadata_extraction.md
pubmed_search.md
scripts/
doi_to_bibtex.py
extract_metadata.py
format_bibtex.py
generate_schematic_ai.py
generate_schematic.py
search_google_scholar.py
search_pubmed.py
validate_citations.py
SKILL.md
clinical-decision-support/
assets/
biomarker_report_template.tex
clinical_pathway_template.tex
cohort_analysis_template.tex
color_schemes.tex
example_gbm_cohort.md
recommendation_strength_guide.md
treatment_recommendation_template.tex
references/
biomarker_classification.md
clinical_decision_algorithms.md
evidence_synthesis.md
outcome_analysis.md
patient_cohort_analysis.md
README.md
treatment_recommendations.md
scripts/
biomarker_classifier.py
build_decision_tree.py
create_cohort_tables.py
generate_schematic_ai.py
generate_schematic.py
generate_survival_analysis.py
validate_cds_document.py
SKILL.md
clinical-reports/
assets/
case_report_template.md
clinical_trial_csr_template.md
clinical_trial_sae_template.md
consult_note_template.md
discharge_summary_template.md
hipaa_compliance_checklist.md
history_physical_template.md
lab_report_template.md
pathology_report_template.md
quality_checklist.md
radiology_report_template.md
soap_note_template.md
references/
case_report_guidelines.md
clinical_trial_reporting.md
data_presentation.md
diagnostic_reports_standards.md
medical_terminology.md
patient_documentation.md
peer_review_standards.md
README.md
regulatory_compliance.md
scripts/
check_deidentification.py
compliance_checker.py
extract_clinical_data.py
format_adverse_events.py
generate_report_template.py
generate_schematic_ai.py
generate_schematic.py
terminology_validator.py
validate_case_report.py
validate_trial_report.py
SKILL.md
cobrapy/
references/
api_quick_reference.md
workflows.md
SKILL.md
consciousness-council/
references/
advanced-configurations.md
SKILL.md
dask/
references/
arrays.md
bags.md
best-practices.md
dataframes.md
futures.md
schedulers.md
SKILL.md
database-lookup/
references/
addgene.md
alphafold.md
alphavantage.md
bea.md
bindingdb.md
biogrid.md
bls.md
brenda.md
cbioportal.md
census.md
chebi.md
chembl.md
clinicaltrials.md
clinpgx.md
clinvar.md
cod.md
cosmic.md
dailymed.md
datacommons.md
dbsnp.md
disgenet.md
drugbank.md
ecb.md
emdb.md
ena.md
encode.md
ensembl.md
epa.md
eurostat.md
fda.md
federal-reserve.md
fred.md
gene-ontology.md
geo.md
gnomad.md
gtex.md
gwas-catalog.md
hca.md
hpo.md
human-protein-atlas.md
interpro.md
jaspar.md
kegg.md
lincs-l1000.md
materials-project.md
metabolomics-workbench.md
monarch.md
mousemine.md
nasa-exoplanet-archive.md
nasa.md
ncbi-gene.md
ncbi-protein.md
ncbi-taxonomy.md
nist.md
noaa.md
omim.md
opentargets.md
openweathermap.md
pdb.md
pride.md
pubchem.md
quickgo.md
reactome.md
retrieval-contract.md
rummageo.md
sdss.md
sec-edgar.md
simbad.md
sra.md
string.md
tcga-gdc.md
treasury.md
ucsc-genome.md
uniprot.md
usgs.md
uspto.md
who.md
worldbank.md
zinc.md
SKILL.md
datamol/
references/
conformers_module.md
core_api.md
descriptors_viz.md
fragments_scaffolds.md
io_module.md
reactions_data.md
SKILL.md
deepchem/
references/
api_reference.md
workflows.md
scripts/
graph_neural_network.py
predict_solubility.py
transfer_learning.py
SKILL.md
deeptools/
assets/
quick_reference.md
references/
effective_genome_sizes.md
normalization_methods.md
tools_reference.md
workflows.md
scripts/
validate_files.py
workflow_generator.py
SKILL.md
depmap/
references/
dependency_analysis.md
SKILL.md
dhdna-profiler/
references/
advanced-profiling.md
SKILL.md
diffdock/
assets/
batch_template.csv
custom_inference_config.yaml
references/
confidence_and_limitations.md
parameters_reference.md
workflows_examples.md
scripts/
analyze_results.py
prepare_batch_csv.py
setup_check.py
... 1525 more© 2026 Flowy · Free and open source
Built for Claude Code · Not affiliated with Anthropic
🔔 Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills standard, not just Claude.
New: K-Dense BYOK — A free, open-source AI co-scientist that runs on your desktop, powered by Scientific Agent Skills. Bring your own API keys, pick from 40+ models, and get a full research workspace with web search, file handling, 100+ scientific databases, and access to all 148 skills in this repo. Your data stays on your computer, and you can optionally scale to cloud compute via Modal for heavy workloads. Get started here.
Stay up to date: Follow K-Dense on X, LinkedIn, and YouTube for new skills, release announcements, walkthroughs, research workflow demos, and examples you can use with your own AI agent.
A comprehensive collection of 148 ready-to-use scientific and research skills (covering cancer genomics, drug-target binding, molecular dynamics, RNA velocity, geospatial science, time series forecasting, scientific ML resource discovery via Hugging Science, 78+ scientific databases, and more) for any AI agent that supports the open Agent Skills standard, created by K-Dense. Works with Cursor, Claude Code, Codex, Google Antigravity, and more. Transform your AI agent into a research assistant capable of executing complex multi-step scientific workflows across biology, chemistry, medicine, and beyond.
⭐ Help make AI for science easier to discover: If Scientific Agent Skills saves you time, teaches your agent a workflow, or helps your lab move faster, please star this repository. A star is a public signal that these open, reusable research skills are worth maintaining: it helps scientists, engineers, and open-source contributors find the project, shows which agent-skill standards are gaining real adoption, and gives us a clear reason to keep expanding the collection for the community.
These skills enable your AI agent to seamlessly work with specialized scientific libraries, databases, and tools across multiple scientific domains. While the agent can use any Python package or API on its own, these explicitly defined skills provide curated documentation and examples that make it significantly stronger and more reliable for the workflows below:
Transform your AI coding agent into an 'AI Scientist' on your desktop!
🎬 New to Scientific Agent Skills? Watch our Getting Started with Scientific Agent Skills video for a quick walkthrough.
This repository provides 148 scientific and research skills organized into the following categories:
Each skill includes:
SKILL.md)Install Scientific Agent Skills with a single command:
npx skills add K-Dense-AI/scientific-agent-skills
This is the official standard approach for installing Agent Skills across all platforms, including Claude Code, Claude Cowork, Codex, Gemini CLI, Google Antigravity, Cursor, OpenClaw, NVIDIA NemoClaw, Hermes, Pi, and any other agent that supports the open Agent Skills standard.
gh skill)If you use the GitHub CLI (v2.90.0+), you can install skills with gh skill:
# Browse and install interactively
gh skill install K-Dense-AI/scientific-agent-skills
# Install a specific skill directly
gh skill install K-Dense-AI/scientific-agent-skills scanpy
# Target a specific agent host
gh skill install K-Dense-AI/scientific-agent-skills --agent cursor
gh skill install K-Dense-AI/scientific-agent-skills --agent claude-code
gh skill install K-Dense-AI/scientific-agent-skills --agent codex
gh skill install K-Dense-AI/scientific-agent-skills --agent gemini
gh skill automatically installs to the correct directory for your agent host and records provenance metadata for supply chain integrity.
Pin to a specific release tag or commit SHA for reproducible installs:
# Pin to a release tag
gh skill install K-Dense-AI/scientific-agent-skills --pin v1.0.0
# Pin to a commit SHA
gh skill install K-Dense-AI/scientific-agent-skills --pin abc123def
# Check for updates interactively
gh skill update
# Update all installed skills
gh skill update --all
You usually don't need anything host-specific. npx skills add (Option 1) installs into the shared ~/.agents/skills/ convention, and any compliant client that scans that directory — including OpenClaw, NVIDIA NemoClaw (an OpenClaw-based secure runtime), and Pi — discovers the skills automatically. Project-scoped installs land in .agents/skills/ and work the same way. To install without the CLI, clone straight into either location:
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git ~/.agents/skills/scientific-agent-skills # user-level
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git .agents/skills/scientific-agent-skills # project-level
Hermes is the one host that uses its own registry instead of the shared directory, so add the repo as a tap:
hermes skills tap add K-Dense-AI/scientific-agent-skills
These skills stay portable across all of them: metadata is single-line JSON (so OpenClaw's line-based reader parses it), credentialed skills declare a top-level required_environment_variables field (so Hermes prompts for keys), and unknown fields are ignored everywhere else. Because 148 skills add up to a lot of standing context, consider installing a topical subset rather than the whole collection.
NemoClaw note: NemoClaw runs agents inside NVIDIA OpenShell with default-deny outbound networking. Skills are discovered and loaded normally, but any skill that needs the network — package installs via
uv, or API calls (Exa, Parallel, Benchling, NCBI, Materials Project, …) — only works once the operator pre-approves the relevant domains in the OpenShell TUI.
That's it! Your AI agent will automatically discover the skills and use them when relevant to your scientific tasks. You can also invoke any skill manually by mentioning the skill name in your prompt.
Skills can execute code and influence your coding agent's behavior. Review what you install.
Agent Skills are powerful — they can instruct your AI agent to run arbitrary code, install packages, make network requests, and modify files on your system. A malicious or poorly written skill has the potential to steer your coding agent into harmful behavior.
We take security seriously. All contributions go through a review process, and we run LLM-based security scans (via Cisco AI Defense Skill Scanner) on every skill in this repository. However, as a small team with a growing number of community contributions, we cannot guarantee that every skill has been exhaustively reviewed for all possible risks.
It is ultimately your responsibility to review the skills you install and decide which ones to trust.
We recommend the following:
SKILL.md before installing. Each skill's documentation describes what it does, what packages it uses, and what external services it connects to. If something looks suspicious, don't install it.K-Dense-AI) have been through our internal review process. Community-contributed skills have been reviewed to the best of our ability, but with limited resources.uv pip install cisco-ai-skill-scanner
skill-scanner scan /path/to/skill --use-behavioral
All skills are scanned on an approximately weekly basis, and SECURITY.md is updated with the latest results. We try to address security gaps as they arise.
Scientific Agent Skills is powered by 50+ incredible open source projects maintained by dedicated developers and research communities worldwide. Projects like Biopython, Scanpy, RDKit, scikit-learn, PyTorch Lightning, and many others form the foundation of these skills.
If you find value in this repository, please consider supporting the projects that make it possible:
SKILL.md files for specific requirements)The skills use uv as the package manager for installing Python dependencies. Install it using the instructions for your operating system:
macOS and Linux:
curl -LsSf https://astral.sh/uv/install.sh | sh
Windows:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Alternative (via pip):
pip install uv
After installation, verify it works by running:
uv --version
For more installation options and details, visit the official uv documentation.
Once you've installed the skills, you can ask your AI agent to execute complex multi-step scientific workflows. Here are some example prompts:
Goal: Find novel EGFR inhibitors for lung cancer treatment
Prompt:
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.
Skills Used: database-lookup, rdkit, datamol, diffdock, paper-lookup, scientific-visualization
Goal: Comprehensive analysis of 10X Genomics data with public data integration
Prompt:
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.
Skills Used: scanpy, cellxgene-census, database-lookup, pydeseq2, arboreto
Goal: Integrate RNA-seq, proteomics, and metabolomics to predict patient outcomes
Prompt:
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.
Skills Used: pydeseq2, pyopenms, database-lookup, statsmodels, scikit-learn
Goal: Discover allosteric modulators for protein-protein interactions
Prompt:
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.
Skills Used: database-lookup, biopython, rdkit, diffdock, deepchem, medchem, molfeat
Goal: Analyze VCF file for hereditary cancer risk assessment
Prompt:
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.
Skills Used: pysam, database-lookup, paper-lookup, clinical-reports, docx, pdf
Goal: Analyze gene regulatory networks from RNA-seq data
Prompt:
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.
Skills Used: database-lookup, torch-geometric, arboreto, pymc, networkx, scientific-visualization
📖 Want more examples? Check out docs/examples.md for comprehensive workflow examples and detailed use cases across all scientific domains.
This repository contains 148 scientific and research skills organized across multiple domains. Each skill provides comprehensive documentation, code examples, and best practices for working with scientific libraries, databases, and tools.
Note: The Python package and integration skills listed below are explicitly defined skills — curated with documentation, examples, and best practices for stronger, more reliable performance. They are not a ceiling: the agent can install and use any Python package or call any API, even without a dedicated skill. The skills listed simply make common workflows faster and more dependable.
A unified database-lookup skill provides deterministic REST API access to 78 public databases across all domains, with retrieval contracts, pagination/count reconciliation, and endpoint provenance. Dedicated skills cover specialized data platforms. Multi-database packages like BioServices (~40 bioinformatics services), BioPython (38 NCBI sub-databases via Entrez), and gget (20+ genomics databases) add further coverage.
datasets, transformers, and gradio_client)📖 For complete details on all skills, see docs/skills.md
💡 Looking for practical examples? Check out docs/examples.md for comprehensive workflow examples across all scientific domains.
We welcome contributions to expand and improve this scientific skills repository!
For detailed instructions on adding or updating a skill, see CONTRIBUTING.md. The guide covers repository structure, required SKILL.md frontmatter, Agent Skills specification requirements, versioning, validation, security scanning, and pull request expectations.
✨ Add New Skills
📚 Improve Existing Skills
🐛 Report Issues
git checkout -b feature/amazing-skill)SKILL.md files with required frontmatter and metadata.versiongit commit -m 'Add amazing skill')git push origin feature/amazing-skill)✅ Adhere to the Agent Skills Specification — Every skill must follow the official spec (valid SKILL.md frontmatter, naming conventions, directory structure)
✅ Include a quoted metadata.version value in every SKILL.md
✅ Increment metadata.version when updating an existing skill
✅ Maintain consistency with existing skill documentation format
✅ Ensure all code examples are tested and functional
✅ Follow scientific best practices in examples and workflows
✅ Update relevant documentation when adding new capabilities
✅ Provide clear comments and docstrings in code
✅ Include references to official documentation
All skills in this repository are security-scanned using Cisco AI Defense Skill Scanner, an open-source tool that detects prompt injection, data exfiltration, and malicious code patterns in Agent Skills.
If you are contributing a new skill, we recommend running the scanner locally before submitting a pull request:
uv pip install cisco-ai-skill-scanner
skill-scanner scan /path/to/your/skill --use-behavioral
Note: A clean scan result reduces noise in review, but does not guarantee a skill is free of all risk. Contributed skills are also reviewed manually before merging.
Contributors are recognized in our community and may be featured in:
Your contributions help make scientific computing more accessible and enable researchers to leverage AI tools more effectively!
This project builds on 50+ amazing open source projects. If you find value in these skills, please consider supporting the projects we depend on.
Problem: Skills not loading
SKILL.md fileProblem: Missing Python dependencies
SKILL.md file for required packagesuv pip install package-nameProblem: API rate limits
Problem: Authentication errors
SKILL.md for authentication setupProblem: Outdated examples
Problem: gh skill install or docs link to scientific-skills/ fails (v2.43.0+)
skills/ (not scientific-skills/) to match the Agent Skills layout expected by GitHub CLIscientific-skills/<name> to skills/<name>gh skill install K-Dense-AI/scientific-agent-skills after pulling the latest releaseQ: Is this free to use?
A: Yes! This repository is MIT licensed. However, each individual skill has its own license specified in the license metadata field within its SKILL.md file—be sure to review and comply with those terms.
Q: Why are all skills grouped together instead of separate packages?
A: We believe good science in the age of AI is inherently interdisciplinary. Bundling all skills together makes it trivial for you (and your agent) to bridge across fields—e.g., combining genomics, cheminformatics, clinical data, and machine learning in one workflow—without worrying about which individual skills to install or wire together.
Q: Can I use this for commercial projects?
A: The repository itself is MIT licensed, which allows commercial use. However, individual skills may have different licenses—check the license field in each skill's SKILL.md file to ensure compliance with your intended use.
Q: Do all skills have the same license?
A: No. Each skill has its own license specified in the license metadata field within its SKILL.md file. These licenses may differ from the repository's MIT License. Users are responsible for reviewing and adhering to the license terms of each individual skill they use.
Q: How often is this updated?
A: We regularly update skills to reflect the latest versions of packages and APIs. Major updates are announced in release notes.
Q: Can I use this with other AI models?
A: The skills follow the open Agent Skills standard and work with any compatible agent, including Cursor, Claude Code, Codex, Google Antigravity, OpenClaw, NVIDIA NemoClaw, Hermes, and Pi.
Q: Do I need all the Python packages installed?
A: No! Only install the packages you need. Each skill specifies its requirements in its SKILL.md file.
Q: What if a skill doesn't work?
A: First check the Troubleshooting section. If the issue persists, file an issue on GitHub with detailed reproduction steps.
Q: Do the skills work offline?
A: Database skills require internet access to query APIs. Package skills work offline once Python dependencies are installed.
Q: Can I contribute my own skills?
A: Absolutely! We welcome contributions. See the Contributing section for guidelines and best practices.
Q: How do I report bugs or suggest features?
A: Open an issue on GitHub with a clear description. For bugs, include reproduction steps and expected vs actual behavior.
Need help? Here's how to get support:
SKILL.md and references/ foldersIf you use Scientific Agent Skills in your research or project, please cite the overall collection and, when relevant, the individual skill or skills that materially supported your work.
The collection citation helps others find the repository, understand the broader skill ecosystem used in your workflow, and credit the maintenance effort behind Scientific Agent Skills. Individual skill citations give more precise credit for the specific package, database, or workflow guidance your agent used.
Recommended practice:
@software{scientific_agent_skills_2026,
author = {{K-Dense Inc.}},
title = {Scientific Agent Skills: A Comprehensive Collection of Scientific Tools for AI Agents},
year = {2026},
url = {https://github.com/K-Dense-AI/scientific-agent-skills},
note = {148 skills covering databases, packages, integrations, and analysis tools}
}
K-Dense Inc. (2026). Scientific Agent Skills: A comprehensive collection of scientific tools for AI agents [Computer software]. https://github.com/K-Dense-AI/scientific-agent-skills
K-Dense Inc. Scientific Agent Skills: A Comprehensive Collection of Scientific Tools for AI Agents. 2026, github.com/K-Dense-AI/scientific-agent-skills.
Scientific Agent Skills by K-Dense Inc. (2026)
Available at: https://github.com/K-Dense-AI/scientific-agent-skills
When citing a specific skill, include the skill name, version from metadata.version in that skill's SKILL.md, and the direct skill URL. For example:
@software{scientific_agent_skills_astropy_2026,
author = {{K-Dense Inc.}},
title = {Astropy Skill for Scientific Agent Skills},
year = {2026},
url = {https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/astropy},
note = {Version 1.0, part of Scientific Agent Skills}
}
Plain text format:
Astropy skill for Scientific Agent Skills, version 1.0.
K-Dense Inc. (2026).
https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/astropy
We appreciate acknowledgment in publications, presentations, or projects that benefit from these skills.
This project is licensed under the MIT License.
Copyright © 2026 K-Dense Inc. (k-dense.ai)
See LICENSE.md for full terms.
⚠️ Important: Each skill has its own license specified in the
licensemetadata field within itsSKILL.mdfile. These licenses may differ from the repository's MIT License and may include additional terms or restrictions. Users are responsible for reviewing and adhering to the license terms of each individual skill they use.