What is phenoml-workflow?

A guided tool for creating and testing PhenoML workflows, which process healthcare data. It covers clinical notes converted into FHIR condition records and patient registration with duplicate detection.

Use it to set up PhenoML workflows, verify FHIR provider connections, process example clinical notes, register patients, and test duplicate handling.

It provides a repeatable setup path for connecting healthcare data services and testing workflows without assembling every step yourself.

phenoml-workflow — Skill for Claude Code by PhenoML

Install

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.

Any agent

npx skills add PhenoML/phenoml-skills --skill phenoml-workflow

Clone the repo

git clone --depth 1 https://github.com/PhenoML/phenoml-skills

Made for: Claude Code.

Or install phenoml-skills, the plugin that ships this one along with the rest of its 2 skills.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/phenoml/phenoml-skills/phenoml-workflow/github.svg)](https://agentmods.dev/skills/phenoml/phenoml-skills/phenoml-workflow)

Your own site

<a href="https://agentmods.dev/skills/phenoml/phenoml-skills/phenoml-workflow"><img src="https://agentmods.dev/badge/skills/phenoml/phenoml-skills/phenoml-workflow/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/phenoml/phenoml-skills/phenoml-workflow"><img src="https://agentmods.dev/badge/skills/phenoml/phenoml-skills/phenoml-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,570 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00031	$0.04570
Opus 5	$0.00015	$0.02285
Sonnet 5	$0.00006	$0.00914
Haiku 4.5	$0.00003	$0.00457

Measured 10d ago against content hash e6a0071a1a44, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

phenoml-workflow 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 10d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/check_env.py, scripts/create_workflow.py, scripts/setup_fhir_provider.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/phenoml-workflow/SKILL.md · 481 lines

How it starts

The opening of the file, as written. The whole thing — 481 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PhenoML Workflow Skill

Instructions

This skill provides an end-to-end guided experience for creating and testing PhenoML workflows. The skill walks users through the complete workflow setup process using executable scripts.

When to Use This Skill

Use this skill when users want to:

Set up a new PhenoML workflow from scratch
Create workflows for processing clinical notes into FHIR Condition resources
Set up patient registration workflows with deduplication
Test workflows with example data
Set up or verify FHIR provider connections

Interactive Workflow Setup Flow

This skill provides a step-by-step interactive experience where the skill gathers information from the user conversationally, then executes reusable Python scripts with that information to create and test workflows.

Step 0: Ensure Dependencies are Installed

Before running any scripts, ensure the required Python packages are installed:
```
pip install python-dotenv phenoml
```
If the user gets import errors when running scripts, guide them to install these packages

Step 1: Check FHIR Provider Setup

First, locate and run check_env.py (search for it using glob **/check_env.py) to check credentials and detect instance type
If SHARED EXPERIMENT is detected (experiment.app.pheno.ml):
- Skip FHIR provider setup entirely - shared experiment uses a pre-configured Medplum sandbox
- The system automatically uses "experiment-default" as the FHIR_PROVIDER_ID
- No FHIR credentials (CLIENT_ID, CLIENT_SECRET, BASE_URL) are required
- Proceed directly to Step 2 (Gather Workflow Requirements)
If on a DEDICATED INSTANCE (e.g., acme.app.pheno.ml), ask the user if they have already created a FHIR provider
If NO:
- Run check_env.py to verify credentials
- If FHIR credentials are missing, guide them to add the credentials to .env with examples:
  - Medplum: FHIR_PROVIDER_BASE_URL=https://api.medplum.com/fhir/R4
  - Athena: FHIR_PROVIDER_BASE_URL=https://api.preview.platform.athenahealth.com/fhir/r4
  - Epic: FHIR_PROVIDER_BASE_URL=https://fhir.epic.com/interconnect-fhir-oauth/api/FHIR/R4
  - Cerner: FHIR_PROVIDER_BASE_URL=https://fhir-myrecord.cerner.com/r4/[tenant-id]
- Run setup_fhir_provider.py to create the provider
- The script will save FHIR_PROVIDER_ID to .env automatically
If YES: Run check_env.py to verify FHIR_PROVIDER_ID is set

Read the full file on GitHub · 481 lines

Files

What ships with it

4 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.

Changes

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.

10d ago First seen · 481 lines · 31 tokens per session scan A e6a0071a1a44

Subscribe to this mod's changes

phenoml-workflow is a skill published in the GitHub repository PhenoML/phenoml-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 4,570 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Data and AI Scientific

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for…

K-Dense-AI/scientific-agent-skills · 66 tokens

pyhealth

Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer…

K-Dense-AI/scientific-agent-skills · 216 tokens

torchdrug

Build and troubleshoot TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.

K-Dense-AI/scientific-agent-skills · 61 tokens

deepspot-m

Generate transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Use when you need spatial gene expression in log1p-CPM for 224x224 tiles at about 20x, want to query protein-coding genes by symbol instead of a fixed panel, or want to run prediction across a whole slide after tiling with…

K-Dense-AI/scientific-agent-skills · 80 tokens

nemo-mbridge-perf-expert-parallel-overlap

Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP.

NVIDIA/skills · 56 tokens

pick-a-pii-model

Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.

maziyarpanahi/openmed · 64 tokens