Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentlas-ai/agentlas-desktop --skill pytdcgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/pytdc)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/pytdc"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/pytdc/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/pytdc"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/pytdc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.03334 |
| Opus 5.5 | $0.00016 | $0.01334 |
| Sonnet 5.5 | $0.00008 | $0.00667 |
| Haiku 4.5 | $0.00004 | $0.00333 |
Grade A, and why
pytdc scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to pytdc — 53 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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==1.15.0excludes Python 3.12, and PyTDC's constrained RDKit release has no CPython 3.13 wheel - PyTDC imports deprecated
pkg_resourcesat runtime. Setuptools 82 removed that module; pin the verified compatibility release setuptools 80.9.0. tdc.readthedocs.iostill 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:
uv venv --python 3.11 .venv-pytdc
uv pip install --dry-run --python .venv-pytdc/bin/python \
"setuptools==80.9.0" "PyTDC==1.1.15"
uv pip install --python .venv-pytdc/bin/python \
"setuptools==80.9.0" "PyTDC==1.1.15"
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.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/datasets.md 7.8 KB
- references/oracles.md 9.3 KB
- references/sources.md 9.9 KB
- references/utilities.md 12 KB
- scripts/_common.py 7.2 KB runs code
- scripts/cache_audit.py 4.5 KB runs code
- scripts/discover_metadata.py 4.9 KB runs code
- scripts/load_and_split_data.py 12 KB runs code
- scripts/molecular_generation.py 13 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 315 lines · 41 tokens per session scan A ca04c0c553f4
pytdc is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 41 tokens to every session and 3,334 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to pytdc, differing in 53 lines, and is treated as a copy.
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