# Per-skill test environments.
#
# `python tests/run_all.py --isolated` builds one throwaway uv environment per
# skill from this file, so a skill whose upstream pins conflict with another's
# never has to share an interpreter. Nothing here is installed into the project
# environment, and `uv sync` is unaffected.
#
# Each skill's `packages` list is the set the skill's SKILL.md, references, and
# scripts actually name -- transitive dependencies are left to the resolver.
# Skills whose bundled tooling is standard-library only have `packages = []`;
# they still get a clean environment with just pytest.
#
# `python` overrides the interpreter for that skill alone. This is the reason
# per-skill environments are worth the trouble: opentrons pins numpy<2 (no
# cp313 wheels), PyTDC needs 3.11, and histolab is tested on 3.10. uv downloads the
# interpreter on demand.
#
# Packages listed under [unavailable] are documented by a skill but cannot be
# installed here at all; the reason is recorded so nobody re-derives it.

[defaults]
python = "3.13"

# --- output formats and writing -------------------------------------------

[skills.pptx-posters]
packages = ["python-pptx==1.0.2", "Pillow==12.3.0", "lxml==6.1.3"]

[skills.liteparse]
packages = ["liteparse==2.15.0"]

[skills.markitdown]
packages = ["markitdown[all]==0.1.8", "markitdown-ocr==0.1.1", "markitdown-mcp==0.0.1a7", "openai==3.22.1"]
python = "3.12"

[skills.latex-posters]
packages = ["requests", "python-dotenv"]

[skills.infographics]
packages = ["requests"]

[skills.generate-image]
# The bundled CLI is standard-library only, and a test enforces that.
packages = []

[skills.markdown-mermaid-writing]
packages = []

[skills.venue-templates]
packages = []

[skills.scientific-writing]
packages = []

[skills.scientific-slides]
packages = [
    "pymupdf",
    "python-pptx",
    "pillow",
    "matplotlib",
    "seaborn",
    "numpy",
    "pypdf",
    "requests",
    "python-dotenv",
]

[skills.scientific-schematics]
packages = ["requests", "python-dotenv"]

[skills.scientific-visualization]
packages = ["matplotlib", "seaborn", "plotly", "pypdf", "pillow", "numpy"]

# --- literature and research workflow -------------------------------------

[skills.citation-management]
# The BibTeX parsing, rendering, and validation layers are standard library
# (scripts/_common.py). `requests` covers Crossref, PubMed, OpenAlex, and the
# DataCite fallback; `scholarly` is needed only by search_google_scholar.py.
packages = [
    "requests",
    "scholarly",
]

[skills.pyzotero]
packages = ["pyzotero", "bibtexparser", "python-dotenv"]

[skills.literature-review]
packages = ["requests", "python-dotenv"]

[skills.research-lookup]
packages = ["requests"]

[skills.exa-search]
packages = ["exa-py", "python-dotenv"]

[skills.parallel-web]
packages = []

[skills.paper-lookup]
packages = []

[skills.paperzilla]
packages = []

[skills.bgpt-paper-search]
packages = []

[skills.ontology-term-resolution]
packages = []

[skills.ncats-arax]
packages = []

[skills.peer-review]
packages = []

[skills.scholar-evaluation]
packages = []

[skills.research-grants]
packages = []

[skills.open-notebook]
packages = ["requests"]

[skills.usfiscaldata]
packages = ["pandas", "requests"]

[skills.market-research-reports]
packages = []

# --- reasoning and planning skills (standard library only) ----------------

[skills.hypothesis-generation]
packages = []

[skills.scientific-brainstorming]
packages = []

[skills.scientific-critical-thinking]
packages = []

[skills.consciousness-council]
packages = []

[skills.what-if-oracle]
packages = []

[skills.experimental-design]
packages = ["numpy", "pandas", "pydoe==1.5.0"]

[skills.iso-standards-readiness]
packages = []

[skills.analytical-method-validation]
packages = []

[skills.clinical-decision-support]
packages = []

[skills.clinical-reports]
packages = []

[skills.treatment-plans]
packages = []

[skills.get-available-resources]
packages = ["psutil"]

[skills.autoskill]
packages = ["httpx", "pyyaml", "sentence-transformers"]

[skills.pi-agent]
packages = []

# --- core data, statistics, and plotting ----------------------------------

[skills.uncertainty-and-units]
packages = ["pint==0.26.1", "uncertainties==3.2.3", "numpy==2.5.3", "scipy==1.18.1"]

[skills.matplotlib]
packages = ["matplotlib==3.11.2", "ipympl", "numpy", "pandas", "scipy"]

[skills.seaborn]
packages = ["seaborn==0.13.2", "matplotlib", "numpy", "pandas", "scipy", "ipywidgets", "statsmodels==0.15.0"]

[skills.polars]
packages = ["polars==1.44.2", "pyarrow", "pandas", "numpy", "sqlalchemy", "fastexcel", "xlsxwriter"]

[skills.dask]
packages = [
    "dask[complete]",
    "dask-ml",
    "dask-jobqueue",
    "dask-kubernetes",
    "pandas",
    "pyarrow",
    "h5py",
    "xarray",
    "zarr",
    "s3fs",
    "gcsfs",
    "scipy",
]

[skills.datalad]
packages = ["datalad==1.6.5", "datalad-container==1.2.6", "git-annex==10.20260901.post1"]

[skills.zarr-python]
packages = ["zarr[remote,cli]==3.4.0", "fsspec==2026.9.0", "s3fs==2026.9.0", "gcsfs==2026.8.1", "h5py==3.16.0", "numpy==2.5.3", "xarray==2026.9.0", "dask[array]==2026.8.0", "numcodecs==0.17.0", "obstore==0.11.1"]

[skills.vaex]
python = "3.12"
packages = ["vaex-core==4.19.0", "vaex-hdf5==0.15.0", "vaex-viz==0.6.0", "vaex-ml==0.19.0", "numpy==2.5.3", "pandas==2.3.3", "pyarrow==25.0.1", "matplotlib==3.11.2", "scikit-learn==1.9.1", "xgboost==3.4.1", "lightgbm==4.7.0", "catboost==1.2.10", "numba==0.68.0", "cachetools==7.2.0"]

[skills.exploratory-data-analysis]
# Companion-file suppression uses an internal tifffile option audited at this release.
packages = ["biopython", "h5py", "numpy", "pandas", "pillow", "polars", "tifffile==2026.9.20"]

[skills.networkx]
packages = [
    "networkx==3.7",
    "geopandas",
    "momepy",
    "matplotlib",
    "pandas",
    "plotly",
    "pyvis",
    "scipy",
]

[skills.scikit-learn]
packages = [
    "scikit-learn==1.9.1",
    "imbalanced-learn",
    "umap-learn",
    "matplotlib",
    "seaborn",
    "numpy",
    "pandas",
]

[skills.statsmodels]
packages = ["statsmodels==0.15.0", "scikit-learn==1.9.1", "matplotlib==3.11.2", "numpy==2.5.3", "pandas==3.0.6", "scipy==1.18.1"]

[skills.statistical-analysis]
python = "3.12"
packages = [
    "statsmodels==0.15.0",
    "pingouin==0.7.0",
    "pymc==6.3.2",
    "arviz==1.3.0",
    "scipy==1.18.1",
    "seaborn",
    "matplotlib",
    "numpy",
    "pandas",
    "scikit-learn",
]

[skills.statistical-power]
packages = ["statsmodels==0.15.0", "lifelines==0.30.3", "pingouin==0.7.0", "matplotlib==3.11.2", "numpy==2.5.3", "pandas==2.3.3", "scipy==1.18.1"]

[skills.pkpd-modeling]
packages = ["numpy==2.5.3", "scipy==1.18.1"]

[skills.pymc]
packages = ["pymc==6.3.2", "arviz==1.3.0", "pytensor==3.3.2", "matplotlib", "numpy", "pandas", "scikit-learn", "h5netcdf", "h5py"]

[skills.shap]
python = "3.12"
packages = ["shap[plots]==0.52.0", "matplotlib", "numpy", "pandas", "scikit-learn"]

[skills.umap-learn]
packages = ["umap-learn==0.5.12", "hdbscan==0.8.44", "matplotlib==3.11.2", "numba==0.68.0", "numpy==2.5.3", "scikit-learn==1.9.1"]

[skills.scikit-survival]
packages = [
    "scikit-survival==0.28.0",
    "scikit-learn>=1.9,<1.10",
    "ecos",
    "osqp",
    "joblib",
    "narwhals",
    "numexpr",
    "numpy",
    "pandas",
    "scipy",
]

[skills.sympy]
python = "3.13"
packages = ["sympy==1.14.0", "mpmath==1.3.0", "numpy==2.5.3", "scipy==1.18.1", "matplotlib==3.11.2", "ipywidgets==8.1.9", "ipython==9.17.1", "antlr4-python3-runtime==4.11.1"]

[skills.pymoo]
packages = ["pymoo==0.6.2", "joblib", "optuna", "matplotlib", "numpy", "dill"]

[skills.simpy]
packages = ["simpy==4.1.2"]

[skills.aeon]
python = "3.13"
packages = ["aeon==1.6.0", "stumpy==1.14.1", "matplotlib", "numpy", "scikit-learn"]

[skills.relsa-severity-assessment]
packages = ["numpy==2.5.3", "pandas==3.0.6", "scipy==1.18.1", "statsmodels==0.15.0", "matplotlib==3.11.2"]

[skills.timesfm-forecasting]
packages = [
    "timesfm[torch]==3.0.2",
    "torch==2.14.1",
    "jax==0.11.2",
    "scikit-learn==1.9.1",
    "matplotlib==3.11.2",
    "numpy==2.5.3",
    "pandas==3.0.6",
    "pillow==12.3.0",
]

# --- machine learning -----------------------------------------------------

[skills.transformers]
python = "3.11"
packages = [
    "transformers==5.18.0",
    "torch==2.14.1",
    "datasets==5.0.1",
    "accelerate==1.15.0",
    "huggingface-hub==1.33.0",
    "peft==0.21.2",
    "pillow==12.3.0",
    "bitsandbytes==0.50.2",
]

[skills.hugging-science]
packages = [
    "transformers",
    "datasets",
    "huggingface-hub",
    "accelerate",
    "gradio-client",
    "torch",
    "python-dotenv",
]

[skills.pytorch-lightning]
python = "3.11"
packages = ["lightning==2.6.6", "torch==2.14.1"]

[skills.torch-geometric]
packages = [
    "torch-geometric==2.8.0.post1",
    "torch==2.14.1",
    "captum==0.9.0",
    "lightning==2.6.6",
    "networkx==3.7",
    "pandas==3.0.6",
    "sentence-transformers==6.1.0",
    "scikit-learn==1.9.1",
]

[skills.stable-baselines3]
packages = ["stable-baselines3==2.9.0", "sb3-contrib==2.9.0", "gymnasium==1.3.0", "numpy", "torch==2.14.1", "tensorboard", "moviepy", "pygame-ce"]

[skills.pufferlib]
packages = []

[skills.modal]
packages = ["modal==1.6.0", "fastapi==0.136.3", "httpx==0.28.1"]

[skills.optimize-for-gpu]
packages = ["numpy==2.5.3"]

[skills.hypogenic]
packages = ["pyyaml==6.0.3"]

[skills.pyhealth]
python = "3.12"
packages = ["pyhealth==2.0.2", "torch==2.7.1"]

# --- single cell and transcriptomics --------------------------------------

[skills.scanpy]
packages = [
    "scanpy[leiden,skmisc]==1.12.4",
    "anndata==0.13.4",
    "harmonypy==2.0.2",
    "bbknn==1.6.0",
    "scikit-image==0.26.0",
    "python-igraph",
    "leidenalg",
    "dask",
    "matplotlib",
    "numpy",
    "pandas",
    "scipy",
]

[skills.anndata]
packages = ["anndata[dask,lazy]==0.13.4", "h5py", "muon", "fsspec", "numpy", "pandas", "scipy", "scikit-learn", "openpyxl", "loompy"]

[skills.scvi-tools]
packages = [
    "scvi-tools==1.5.1",
    "scanpy==1.12.4",
    "mudata==0.4.1",
    "igraph",
    "matplotlib",
    "numpy",
    "pandas",
    "scipy",
    "scikit-learn",
]

[skills.scvelo]
packages = [
    "scvelo==0.3.4",
    "scanpy==1.12.4",
    "anndata==0.13.4",
    "pandas==2.3.3",
    "matplotlib==3.11.2",
    "numpy==2.5.3",
    "scipy==1.18.1",
    "loompy==3.0.8",
]

[skills.cellxgene-census]
python = "3.12"
packages = ["cellxgene-census==1.18.0", "tiledbsoma", "tiledbsoma-ml==0.1.0", "scanpy", "spatialdata", "scanorama"]

[skills.pydeseq2]
python = "3.13"
packages = ["pydeseq2==0.5.4", "anndata==0.13.4", "matplotlib", "numpy", "pandas"]

[skills.bulk-rnaseq]
packages = ["pydeseq2", "pytximport", "gseapy", "gprofiler-official", "pandas", "numpy"]

[skills.pathway-enrichment]
packages = ["gseapy==1.3.1", "gprofiler-official==1.0.0", "mygene==3.2.2", "numpy", "pandas", "lxml"]

[skills.arboreto]
python = "3.11"
# Published Arboreto still requires legacy Dask dataframe and SciPy sparse .A.
packages = ["arboreto==0.1.6", "dask[complete]==2024.7.1", "distributed==2024.7.1", "numpy==1.26.4", "pandas==2.2.3", "scikit-learn==1.5.2", "scipy==1.13.1"]

[skills.depmap]
packages = ["numpy", "pandas", "requests", "scipy"]

[skills.primekg]
packages = ["pandas"]

# --- genomics and sequence analysis ---------------------------------------

[skills.genomic-coordinates]
packages = []

[skills.pathogen-variant-surveillance]
packages = []

[skills.biopython]
packages = ["biopython", "matplotlib", "numpy", "reportlab"]

# Primer design and nearest-neighbor thermodynamics; BLAST+ is an optional system tool.
[skills.primer-design]
python = "3.13"
packages = ["primer3-py==2.3.1"]

[skills.pysam]
packages = ["pysam==0.24.1"]

[skills.bioservices]
packages = ["bioservices", "biopython", "networkx", "pandas"]

[skills.gget]
# scanpy is pinned forward deliberately. Unpinned, the resolver walks it back to
# 1.9.8, whose numba 0.53.1 needs llvmlite 0.36.0, which cannot build on 3.13 --
# so the whole environment fails before a single test runs. >=1.10 is also what
# gget's own cellxgene extra requires.
packages = ["gget", "matplotlib", "numpy", "pandas", "scanpy>=1.10"]

[skills.scikit-bio]
packages = ["scikit-bio==0.7.4", "biom-format", "tables", "matplotlib", "numpy", "pandas", "seaborn"]

[skills.polars-bio]
packages = ["polars-bio==0.36.0", "polars==1.44.2", "pandas", "bioframe"]

# waypoint-bio's own package pulls torch + transformers + datasets, which the
# bundled scripts deliberately do not require: the converter is pandas-only and
# vocab_coverage.py lazy-imports the tokenizer so --help works without it.
[skills.waypoint-bio]
packages = ["pandas==3.0.6", "pyarrow==25.0.1"]

[skills.gtars]
packages = ["gtars==0.10.0"]

[skills.geniml]
python = "3.12"
packages = [
    "geniml==0.8.4", "gtars==0.10.0", "pyarrow==25.0.1", "scanpy==1.12.4",
    "anndata==0.12.19", "gensim==4.4.0", "torch==2.14.1", "huggingface-hub==2.0.0",
    "pyBigWig==0.3.26", "hmmlearn==0.3.3", "qdrant-client==1.19.1",
]

[skills.deeptools]
packages = ["deeptools==4.0.0"]
python = "3.13"

[skills.flowio]
packages = ["flowio==1.4.0", "numpy==2.5.3", "pandas==3.0.6"]

[skills.flowkit]
python = "3.13"
packages = ["flowkit==1.3.2"]

[skills.etetoolkit]
packages = ["ete4==4.4.0"]

[skills.phylogenetics]
python = "3.12"  # ete3 3.1.3 imports cgi, removed in 3.13
packages = ["ete3==3.1.3", "numpy", "six"]

[skills.onekgpd]
packages = ["dnaerys>=0.2.1,<0.3.0"]

[skills.genomic-intelligence]
packages = ["requests"]

[skills.alphagenome]
packages = ["alphagenome"]

[skills.dhdna-profiler]
packages = []

[skills.glycoengineering]
# Bundled helpers/API recipes; GlycoSHIELD has a separate upstream environment.
packages = ["requests==2.34.2"]

[skills.esm]
python = "3.12"
packages = ["esm==3.4.1.post1"]

[skills.diffdock]
# Bundled input-preparation helper only; full inference uses upstream's environment.
packages = ["pandas", "rdkit"]

# --- chemistry and materials ----------------------------------------------

[skills.rdkit]
packages = ["rdkit==2026.3.6"]

[skills.datamol]
packages = ["datamol==0.13.0", "rdkit==2026.3.6", "s3fs", "gcsfs"]

[skills.medchem]
packages = ["medchem==2.1.1", "datamol==0.13.0", "rdkit==2026.3.6", "pandas", "tqdm"]

[skills.molfeat]
python = "3.13"
packages = ["molfeat==1.0.0", "datamol==0.13.0"]

[skills.matchms]
packages = ["matchms==0.33.1", "numpy"]

[skills.pyopenms]
python = "3.13"
packages = ["pyopenms==3.6.0", "matplotlib", "numpy", "pandas"]

[skills.molecular-dynamics]
packages = ["mdanalysis==2.10.0", "openmm==8.6.1", "pdbfixer==1.12.0", "matplotlib", "numpy", "pandas", "scipy"]

[skills.pymatgen]
# Pinned to the skill's own "Verified snapshot (2026-09-30)", which its
# `_common.py` enforces before any network execution. Left unpinned, a new
# pymatgen-core release makes the suite fail on the snapshot check -- and
# loosening that check would throw away the guarantee it exists to make. When
# the skill re-verifies against newer releases, update both together.
packages = ["pymatgen==2026.9.24", "pymatgen-core==2026.9.23", "mp-api==0.46.5"]

[skills.rowan]
packages = ["rowan-python", "rdkit", "pandas"]

[skills.deepchem]
python = "3.11"
# Tested CPU paths include dense regression, DMPNN, local Hugging Face models,
# and GROVER. Torch GCN/GAT/MPNN need DGL/DGLLifeSci, verified separately in docs.
packages = [
    "deepchem==2.8.0", "torch==2.14.1", "transformers==5.18.0",
    "torch-geometric==2.8.0.post1", "numpy", "scikit-learn",
]

[skills.pytdc]
python = "3.11"
packages = ["pytdc==1.1.15", "setuptools==80.9.0"]

[skills.cobrapy]
packages = ["cobra", "matplotlib", "pandas", "seaborn"]

[skills.13c-metabolic-flux]
python = "3.12"
# mfapy is GitHub-only; pin the reviewed source, not a moving branch.
packages = [
    "mfapy @ git+https://github.com/fumiomatsuda/mfapy.git@a10433af16682386548b360297e2476152d46ede",
    "numpy==2.5.3",
    "scipy==1.18.1",
    "nlopt==2.11.0",
]

# --- medical imaging and clinical data ------------------------------------

[skills.pydicom]
packages = [
    "pydicom==3.0.2",
    "numpy==2.5.3",
    "pillow==12.3.0",
    "pylibjpeg==2.1.0",
    "pylibjpeg-libjpeg==2.4.0",
    "pylibjpeg-openjpeg==2.6.0",
    "pylibjpeg-rle==2.2.0",
    "pyjpegls==1.5.1",
    "python-gdcm==3.2.6",
]

[skills.bids]
packages = ["pybids", "nibabel", "pydicom", "heudiconv", "dcm2bids", "bids-validator-deno==3.0.2"]

[skills.imaging-data-commons]
packages = [
    "idc-index",
    "duckdb",
    "google-cloud-bigquery[pandas]",
    "pydicom",
    "simpleitk",
    "numpy",
    "pandas",
    "s3fs",
    "gcsfs",
]

[skills.pacsomatic]
packages = []

[skills.histolab]
python = "3.10"
packages = ["histolab==0.7.0", "pooch", "matplotlib"]

[skills.omero-integration]
packages = ["numpy", "pillow"]

# --- neuroscience ---------------------------------------------------------

[skills.neurokit2]
packages = ["neurokit2==0.2.13", "numpy==2.5.3", "pandas==2.3.3"]

[skills.neuropixels-analysis]
packages = ["spikeinterface==0.105.0", "probeinterface==0.4.0", "neo==0.14.5", "matplotlib", "numpy", "pandas", "scipy", "numba", "scikit-learn"]

[skills.arbor]
# Hypothesis Tree Refinement ships stdlib tools; not the arbor neural simulator.
packages = []

# --- quantum computing ----------------------------------------------------

[skills.qiskit]
packages = [
    "qiskit[visualization]==2.5.2",
    "qiskit-ibm-runtime==0.50.0",
    "qiskit-aer==0.17.2",
    "qiskit-algorithms==0.4.0",
    "matplotlib",
]

[skills.cirq]
packages = [
    "cirq==1.7.0",
    "cirq-aqt==1.7.0",
    "cirq-google==1.7.0",
    "cirq-ionq==1.7.0",
    "cirq-pasqal==1.7.0",
    "matplotlib",
    "sympy",
    "ply",
]

[skills.pennylane]
python = "3.13"
packages = [
    "pennylane==0.45.1",
    "pennylane-lightning==0.45.0",
    "jax==0.7.1",
    "jaxlib==0.7.1",
    "torch==2.14.1",
    "networkx==3.7",
]

[skills.qutip]
packages = ["qutip==5.3.1", "qutip-qip==0.4.2", "qutip-qtrl==0.2.0", "qutip-jax==0.1.1", "matplotlib", "numpy"]

# --- physics, astronomy, and simulation -----------------------------------

[skills.astropy]
packages = ["astropy", "matplotlib", "numpy", "pandas", "dask", "scipy", "h5py"]

[skills.fluidsim]
packages = ["fluidsim", "fluidfft", "pyfftw", "h5py", "numpy"]

[skills.openpiv]
packages = ["openpiv==0.26.1", "numpy==2.5.3", "scipy==1.18.1", "scikit-image==0.26.0", "matplotlib==3.11.2"]

[skills.lab-hardware-cad]
# Native geometry and export checks use build123d's OpenCascade kernel in
# addition to the standard-library standards and fit-arithmetic regressions.
python = "3.12"
packages = ["build123d==0.13.0", "matplotlib"]

[skills.fictiv]
# check_cad_file.py is standard library only; the STEP fixtures are committed
# build123d exports, so the suite needs no CAD kernel. The browser scripts are
# syntax-checked with node when it is on PATH.
packages = []

# --- chemical oceanography ------------------------------------------------

[skills.marine-carbonate-chemistry]
python = "3.13"
packages = ["PyCO2SYS==1.8.3.4", "numpy==2.5.3"]

# --- geospatial -----------------------------------------------------------

[skills.geopandas]
python = "3.12"
packages = [
    "geopandas==1.2.0",
    "numpy==2.5.3",
    "packaging==26.3",
    "pandas==3.0.6",
    "pyarrow==25.0.1",
    "pyogrio==0.13.0",
    "pyproj==3.8.0",
    "shapely==2.1.2",
    "matplotlib==3.11.2",
    "sqlalchemy==2.1.1",
]

[skills.geomaster]
packages = [
    "geopandas==1.2.0",
    "shapely==2.1.2",
    "pyproj==3.8.0",
    "rasterio==1.5.2",
    "rioxarray==0.23.0",
    "xarray==2026.9.0",
    "dask[array]",
    "scipy",
    "scikit-learn==1.9.1",
    "osmnx==2.1.1",
    "pystac-client==0.9.0",
    "planetary-computer==1.0.0",
    "odc-stac",
    "pyarrow",
    "zarr",
    "movingpandas",
    "pykrige",
    "esda==2.10.0",
    "rio-cogeo",
    "dask-geopandas==0.5.0",
    "fiona==1.10.1",
    "mercantile==1.2.1",
    "scikit-image==0.26.0",
]

# --- lab platforms and integrations ---------------------------------------

[skills.benchling-integration]
packages = ["benchling-sdk==1.25.0", "biopython", "boto3", "httpx"]

[skills.dnanexus-integration]
packages = ["dxpy>=0.400"]

[skills.latchbio-integration]
packages = ["latch"]

[skills.labarchive-integration]
packages = []

[skills.lamindb]
python = "3.12"
packages = ["lamindb==2.10.0", "bionty==2.5.0", "ipython==9.17.1"]

[skills.opentrons-integration]
python = "3.12"
packages = ["opentrons==10.0.0"]

[skills.pylabrobot]
packages = ["pylabrobot==0.2.2"]

[skills.tiledbvcf]
python = "3.12"
packages = ["tiledb==0.36.1", "tiledb-cloud==0.14.4"]

[skills.nextflow]
packages = ["nf-core"]

[skills.tamarind]
packages = ["tamarind", "requests"]

[skills.adaptyv]
packages = []

[skills.ginkgo-cloud-lab]
packages = []

[skills.protocolsio-integration]
packages = []

[skills.database-lookup]
packages = ["zeep"]

[skills.matlab]
packages = ["h5py", "scipy"]

[skills.pathml]
python = "3.12"
packages = []

[skills.torchdrug]
python = "3.10"
# Native extensions need torch in their isolated build environments. A supported
# compiler is still required; see the skill's platform-specific build guidance.
uv_config = "tests/torchdrug/uv.toml"
packages = ["torchdrug==0.2.1", "torch==2.0.0", "torch-scatter==2.1.2", "torch-cluster==1.6.3", "numpy==1.26.4", "scipy==1.13.1", "rdkit-pypi==2022.9.5", "fair-esm==2.0.0", "decorator==5.1.1", "setuptools==80.10.2"]

# --- additional executable scientific workflows --------------------------

[skills.nmrglue]
python = "3.12"
packages = ["nmrglue==0.12", "numpy==2.5.3", "scipy==1.18.1"]

[skills.mageck]
python = "3.11"
packages = []

[skills.cantera]
python = "3.12"
packages = ["cantera==3.2.0", "numpy==2.5.3"]

[skills.cellprofiler]
python = "3.12"
packages = ["numpy==2.5.3", "tifffile==2026.9.20"]

[skills.nwb-conversion]
python = "3.12"
packages = [
    "neuroconv[tiff]==0.10.2",
    "pynwb==4.2.0",
    "nwbinspector==0.7.2",
    "roiextractors==0.10.0",
    "tifffile==2026.9.20",
    "zarr==2.18.7",
    "hdmf-zarr==0.11.3",
]

[skills.pycalphad]
python = "3.12"
packages = ["pycalphad==0.11.2", "numpy==2.5.3"]

[skills.pybamm]
python = "3.12"
packages = ["pybamm==26.9.0.0", "pybammsolvers==0.10.0"]

[skills.qiime2-amplicon]
python = "3.12"
packages = []

[skills.relion]
python = "3.12"
packages = ["numpy==2.5.3", "mrcfile==1.5.4", "starfile==0.5.13"]

[skills.tellurium]
python = "3.11"
packages = [
    "tellurium==2.2.13.1", "libroadrunner==2.10.0", "antimony==3.2.0",
    "python-libsbml==5.21.2", "python-libsedml==2.0.34", "python-libcombine==0.2.20",
]

# --- packages a skill documents that cannot be installed here -------------
#
# The runner prints these when it sets up the skill, so the gap is visible in
# the test output rather than rediscovered each time.

[unavailable]
qiime2-amplicon = "qiime2-amplicon skill: QIIME 2 2026.7 requires the full conda/container distribution including R and DADA2, rather than a pip installation. The portable helper suite is standard-library only."
relion = "relion skill: RELION 5.0.1 native C++ executables are not supplied by the pip test environment; the documented MPI workflows need a compatible MPI/OpenMP runtime and FFTW or MKL. Python validators are tested separately."
cellprofiler = "cellprofiler skill: CellProfiler 4.2.8 needs Java and native system dependencies. Real integration was tested in the official linux/amd64 Docker image; the portable helper suite needs NumPy and tifffile only."
mageck = "mageck skill: MAGeCK 0.5.9.5 requires a source or Bioconda installation with its compiled RRA binary. Put the executable on PATH to run real integration tests; the bundled validation helpers use only the standard library."
adaptyv-sdk = "adaptyv skill: distributed from GitHub only, not on PyPI"
tiledbvcf = "tiledbvcf skill: native tiledbvcf 0.40.3 is not on PyPI; install tiledb::tiledbvcf-py from the official TileDB Conda channel (osx-arm64/osx-64/linux-64, Python 3.9-3.12), or build source. Native tests run separately from uv."
matlabengine = "matlab skill: requires a local MATLAB installation to build against"
hypogenic = "hypogenic skill: pins torch<=2.4.1, which has no cp313 wheels"
pathml = "pathml skill: python-javabridge needs a JDK, plus OpenSlide and Bio-Formats"
omero-py = "omero-integration skill: zeroc-ice 3.6.5 has no wheels and needs a C++ build"
rsgislib = "geomaster skill: native RSGISLib stack via conda-forge/source, unavailable in uv-only test environment"
pdal = "geomaster skill: PyPI extension requires separately installed native PDAL library; conda-forge supported"
mpi4py = "fluidsim skill: needs a site MPI implementation and compilers"
"flash-attn" = "esm skill: needs a CUDA toolchain to build"
deepspeed = "pytorch-lightning skill: Lightning 2.6.6 DeepSpeedStrategy requires CUDA GPUs unavailable on the macOS audit host; optional compiled ops require platform-specific toolchains. A pip installation does not always require CUDA compilation."
"cudf-cu12" = "optimize-for-gpu skill: RAPIDS runtime requires supported Linux x86_64/aarch64 or WSL2 plus NVIDIA CUDA; unavailable on the macOS audit host. CPU algorithm tests use NumPy; Warp separately provides a macOS CPU wheel."