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.
npx skills add K-Dense-AI/scientific-agent-skills --skill pytdc --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 `pkg_resources` 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 an
- Verified snapshot
- Installation
- Non-negotiable data and network policy
- Cache and cost behavior
- Start with metadata-only discovery
- Dataset workflow
- Split selection without overclaiming leakage control
- Evaluators
- Benchmark groups
- Molecular generation and oracles
- Bundled resources
- Scripts
- References
uv venv --python 3.11 .venv-pytdc uv pip install --dry-run --python .venv-pytdc/bin/python \ uv pip install --python .venv-pytdc/bin/python \ uv run --python 3.11 \ python scripts/discover_metadata.py --kind tasks uv run --python 3.11 --with "setuptools==80.9.0" --with "PyTDC==1.1.15" \ python scripts/discover_metadata.py --kind datasets --task ADME --limit 50 python scripts/discover_metadata.py --kind benchmarks --limit 50 python scripts/discover_metadata.py --kind evaluators --limit 100 python scripts/load_and_split_data.py \
What does the pytdc skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill pytdc --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.