Agent skill · research science · k-dense-ai
pytdc
Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits, evaluator metrics, benchmark groups, and bounded molecular-oracle workflows.
Why this skill is useful
Provides specific commands and workflows for using the PyTDC package to access therapeutic datasets and evaluate ML tasks, which the AI wouldn't reliably generate on its own.
What it needs
Requires setuptools, uv installed locally. About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
PyTDC (Therapeutics Data Commons) Use the official PyTDC distribution (import tdc) to discover therapeutic ML tasks, load approved datasets, apply task-appropriate splits, evaluate predictions, and work with curated benchmark groups. Prefer package metadata over copied dataset lists, and plan network/storage effects before constructing any loader. Verified snapshot Research date: 2026-07-23 PyPI stable: PyTDC 1.1.15, released 2025-03-31 Package/source repository: mims-harvard/TDC Code license: MIT PyPI supplies only a source distribution and declares no Requires-Python The dependency graph makes CPython 3.11 the reproducible target used here: cellxgene-census==1.15.0 excludes Python 3.12, and PyTDC's constrained RDKit release has no CPython 3.13 wheel PyTDC imports deprecated pkgresources at runtime. Setuptools 82 removed that module; pin the verified compatibility release setuptools 80.9.0. tdc.readthedocs.io still identifies itself as TDC 0.4.1; use it as API cross-reference, not as release-version evidence Upstream publishes no GitHub tags/releases or maintained changelog. Treat undocumented migration claims as uncertainty and verify against the installed 1.1.15 source/metadata. See references/sources.md for dated evidence and known documentation conflicts. Installation Use an isolated CPython 3.11 environment and pin the reviewed snapshot: The tested macOS ARM64 resolution installed 123 packages, including large scientific/ML dependencies, so the environment itself can transfer and occupy hundreds of megabytes before any dataset is downloaded. Review the dry run and available disk first. The direct pins identify the reviewed API snapshot; generate a platform-specific uv.lock in the user's project when every transitive version must also be frozen. For an ephemeral command: To check for a newer release, inspect the PyPI release history at <https://pypi.org/project/pytdc/>. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/pytdc