# 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: PyTDC and DeepChem need 3.11,
# molfeat caps at 3.10, and AiZynthFinder will not install above 3.12. 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"

# --- knowledge graphs and target discovery --------------------------------

[skills.ncats-arax]
packages = []

# Standard-library-only API clients: the whole point of these three is that a
# retrieval layer should not need an install to work.
[skills.open-targets]
packages = []

[skills.chembl]
packages = []

[skills.uniprot-rcsb]
packages = []

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

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

# --- proteins: design, folding, and docking -------------------------------

[skills.glycoengineering]
packages = ["glycoshield", "pandas", "requests"]

[skills.esm]
packages = ["esm", "torch", "matplotlib", "numpy", "scikit-learn", "requests", "tqdm"]

[skills.diffdock]
packages = ["torch", "rdkit", "openmm", "esm", "pandas"]

# ANARCI is on PyPI but needs HMMER (hmmscan) as a system binary; without it
# the numbering tests skip and the standard-library ones still run.
[skills.antibody-engineering]
packages = ["anarci"]

# --- small molecules: cheminformatics and molecular ML --------------------

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

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

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

[skills.molfeat]
python = "3.10"
packages = ["molfeat", "datamol", "torch"]

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

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

# The bundled helpers are standard-library only; the docking and folding
# engines themselves are external (see [unavailable]).
[skills.autodock-vina]
packages = []

[skills.boltz]
packages = []

[skills.deepchem]
python = "3.11"
# torch is what the skill's own `compatibility` line and the `deepchem[torch]`
# install command require: without a backend, dc.models.GCNModel and friends do
# not exist, so no model path is reachable at all.
packages = ["deepchem", "torch", "numpy", "scikit-learn"]

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

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

[skills.adaptyv]
packages = []

# --- design, synthesis, and chemical space ---------------------------------
#
# Each of these drives a heavy external tool (REINVENT4, AiZynthFinder, OpenFE,
# fpocket, admet-ai). The bundled scripts only build its input and parse its
# output, so the suites stay standard-library-only and run without the tool.

[skills.generative-design]
packages = []

[skills.retrosynthesis]
packages = []

[skills.chemical-space]
packages = []

[skills.admet-prediction]
packages = []

[skills.free-energy-perturbation]
packages = []

[skills.binding-site-analysis]
packages = []

# --- clinical, regulatory, and translational -------------------------------

[skills.clinicaltrials]
packages = []

[skills.openfda]
packages = []

[skills.target-safety]
packages = []

[skills.patent-landscape]
packages = []

[skills.pkpd-translation]
packages = []

# --- modalities beyond small molecules -------------------------------------

[skills.protein-binder-design]
packages = []

[skills.degraders]
packages = []

[skills.immunogenicity]
packages = []

[skills.oligonucleotides]
packages = []

# --- 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]
adaptyv-sdk = "adaptyv skill: distributed from GitHub only, not on PyPI"
dgl = "molfeat skill: no cp313 wheels"
"flash-attn" = "esm skill: needs a CUDA toolchain to build"
"lilly-medchem-rules" = "medchem skill: conda-forge only; the Lilly demerit filter is optional"
vina = "autodock-vina skill: the docking engine is a binary (conda-forge or a release); the bundled scripts do not import it"
meeko = "autodock-vina skill: installs from PyPI but is only used through its command-line scripts"
boltz = "boltz skill: pulls a CUDA torch stack and caps at Python <3.13; the bundled scripts only read and write its files"
hmmer = "antibody-engineering skill: a system binary (conda-forge/homebrew), not a Python package; ANARCI needs hmmscan on PATH"