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 Lord1Egypt/scientific-agent-toolkit --skill pyhealthgit clone --depth 1 https://github.com/Lord1Egypt/scientific-agent-toolkitWrote 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/lord1egypt/scientific-agent-toolkit/pyhealth)<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/pyhealth"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/pyhealth/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/lord1egypt/scientific-agent-toolkit/pyhealth"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/pyhealth.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 | $0.00216 | $0.01831 |
| Opus 5 | $0.00108 | $0.00915 |
| Sonnet 5 | $0.00043 | $0.00366 |
| Haiku 4.5 | $0.00022 | $0.00183 |
Grade A, and why
pyhealth 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 8d 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
95% identical to pyhealth — 3 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyHealth
PyHealth (https://pyhealth.dev/) is a Python toolkit for clinical deep learning. It provides a unified, modular pipeline across electronic health records (EHR), physiological signals, and medical imaging.
The library is built around a 5-stage pipeline — Dataset → Task → Model → Trainer → Metrics — where each stage is replaceable and the interfaces between stages are stable. Code that follows this pipeline shape composes well; code that bypasses it usually fights the library.
When to use this skill
Use this skill whenever the user is doing clinical/healthcare ML and any of the following are true:
- They mention PyHealth, MIMIC-III/IV, eICU, OMOP-CDM, EHRShot, SleepEDF, SHHS, ISRUC, COVID19-CXR, ChestX-ray14, TUEV/TUAB.
- They want to predict mortality, readmission, length of stay, drug recommendations, sleep stages, ICD codes, EEG events, or de-identification.
- They need to look up or cross-map medical codes (ICD-9-CM, ICD-10-CM, ATC, NDC, RxNorm, CCS).
- They have EHR-shaped data and want to train a clinical model without writing the plumbing themselves.
PyHealth is the right tool when the workflow fits its 5 stages. If the user just wants generic PyTorch on tabular data, this skill is not necessary.
Installation (uv)
PyHealth 2.0 requires Python ≥ 3.12, < 3.14. Use uv for environment management — it's faster and reproducible.
# Create a project with the right Python
uv init my-pyhealth-project
cd my-pyhealth-project
uv python pin 3.12
# Add PyHealth (this also pulls in PyTorch and friends)
uv add pyhealth
# Run scripts inside the env
uv run python train.py
For a one-off script without a project, use uv run --with pyhealth python script.py. For the legacy 1.x line (Python 3.9+), uv add pyhealth==1.16. Detailed install notes, MIMIC access, and GPU/CPU device tips are in references/installation.md.
The 5-stage pipeline
A complete pipeline is typically <20 lines. This is the canonical shape — start here and modify pieces:
What ships with it
7 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.
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.
- 8d ago First seen · 124 lines · 216 tokens per session scan A ef6105c39c32
pyhealth is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (3 stars, last pushed 3mo ago), licensed MIT. It adds 216 tokens to every session and 1,831 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to pyhealth, differing in 3 lines, and is treated as a copy.
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