Pyhealth

What it does

When your agent uses it

• PyHealth dataset loading
• OMOP prediction tasks
• Patient-level evaluation
• Mortality/readmission/length-of-stay modeling

Example prompts

• “Use the pyhealth skill to build and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes”
• “/pyhealth”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires Python 3.12 or 3.13 and PyHealth 2.0.2 (Torch 2.7.1). Network access is needed for installation, public datasets and uncached medical-code resources. Restricted clinical datasets require authorized local access.

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

1. 1Inspect the installed version and dataset configuration. Both MIMIC-III and
2. 2Check task semantics and required tables. MortalityPredictionMIMIC3 predicts
3. 3Create supervised samples. base.set_task(task) returns a processed dataset
4. 4Partition by patient and verify overlap, class counts and observation windows.
5. 5Choose a schema-compatible model and run one batch before training. Construct
6. 6Declare validation metrics and the exact monitor. Supply metrics=[...] to
7. 7Evaluate the held-out test set once the model choice is fixed. Report prevalence,

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • uv

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • storage.googleapis.com

  Also links to:

  • arxiv.org
  • doi.org
  • export.arxiv.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires Python 3.12 or 3.13 and PyHealth 2.0.2 (Torch 2.7.1). Network access is needed for installation, public datasets and uncached medical-code resources. Restricted clinical datasets require authorized local access.

  From compatibility in the SKILL.md frontmatter.

Context cost

Pyhealth loads about 2.1k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 801 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~92

When it runs · the whole SKILL.md, loaded when a task matches

~2.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~10k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 801 words, ~2,057 tokens.

name

pyhealth

description

Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.

compatibility

Requires Python 3.12 or 3.13 and PyHealth 2.0.2 (Torch 2.7.1). Network access is needed for installation, public datasets and uncached medical-code resources. Restricted clinical datasets require authorized local access.

metadata.version

1.3

metadata.skill-author

K-Dense Inc.

metadata.last-reviewed

2026-09-30

uv run --no-project --isolated --python 3.12 --with pyhealth==2.0.2 python assets/starter_pipeline.py --demo --epochs 1

from pyhealth.datasets import MIMIC3Dataset, get_dataloader, split_by_patient
from pyhealth.tasks import MortalityPredictionMIMIC3
from pyhealth.models import Transformer
from pyhealth.trainer import Trainer

base = MIMIC3Dataset(
    root="https://storage.googleapis.com/pyhealth/Synthetic_MIMIC-III/",
    tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
    cache_dir="./cache/mimic3", num_workers=1, dev=True,
)
samples = base.set_task(MortalityPredictionMIMIC3())
train, val, test = split_by_patient(samples, [0.6, 0.2, 0.2], seed=42)
loaders = [get_dataloader(part, batch_size=16, shuffle=(i == 0))
           for i, part in enumerate((train, val, test))]
model = Transformer(dataset=train, embedding_dim=8)
trainer = Trainer(model=model, metrics=["accuracy"], device="cpu")
trainer.train(train_dataloader=loaders[0], val_dataloader=loaders[1],
              epochs=1, monitor="accuracy", monitor_criterion="max")
print(trainer.evaluate(loaders[2]))

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references, assets) in skills/pyhealth of K-Dense-AI/scientific-agent-skills.

• SKILL.md
• assets/starter_pipeline.py
• references/datasets.md
• references/examples.md
• references/installation.md
• references/medcode.md
• references/models.md
• references/tasks.md

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

• lingxling/awesome-skills-cnclaude-scientific-skills/skills/pyhealth