OpenMed is local-first healthcare AI software that extracts clinical information and removes personally identifying details from clinical text on hardware controlled by the user. Healthcare developers use its Python runtime, Apple Silicon and mobile SDKs, and browser support for on-device clinical NER and PII de-identification.
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 maziyarpanahi/openmed --skill exporting-bulk-fhirgit clone --depth 1 https://github.com/maziyarpanahi/openmedWrote 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/maziyarpanahi/openmed/exporting-bulk-fhir)<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/exporting-bulk-fhir"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/exporting-bulk-fhir/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/maziyarpanahi/openmed/exporting-bulk-fhir"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/exporting-bulk-fhir.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 42 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00151 | $0.01999 |
| Opus 5 | $0.00076 | $0.01000 |
| Sonnet 5 | $0.00030 | $0.00400 |
| Haiku 4.5 | $0.00015 | $0.00200 |
Grade A, and why
exporting-bulk-fhir scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X GET \ How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exporting Bulk FHIR
When you need cohort-scale clinical text — not one patient in a UI — you use
the FHIR Bulk Data Access ($export) operation: an async job that emits
NDJSON files of resources you then stream into OpenMed for batch
de-identification and NER. This skill sits before the OpenMed pipeline: it is
how the notes arrive.
When to use
Reach for it when the source is an EHR or FHIR data warehouse and the volume is
a population/group (thousands of patients), the workload is headless (no
clinician UI), and the goal is to batch-feed openmed.deidentify /
openmed.analyze_text. Triggers: "bulk export", "$export", "NDJSON", "Flat
FHIR", "cohort de-identification", "export all notes". For a single in-chart
patient with a UI, use scaffolding-smart-on-fhir instead.
Three export levels
- System —
GET [base]/$export— everything the client is authorized for. - Group —
GET [base]/Group/[id]/$export— a defined cohort (most common). - Patient —
GET [base]/Patient/$export— all patients in scope.
Bulk export uses SMART Backend Services auth (a system/*.read-scoped
client-credentials token via a signed JWT assertion), not an interactive launch.
Quick start: kickoff → poll → download
# 1) Kickoff (async). Ask for clinical-note-bearing resource types.
curl -s -X GET \
'https://ehr.example/fhir/Group/cohort-42/$export?_type=DocumentReference,DiagnosticReport&_since=2024-01-01T00:00:00Z' \
-H 'Authorization: Bearer <backend-services-token>' \
-H 'Accept: application/fhir+json' \
-H 'Prefer: respond-async' -D -
# -> 202 Accepted
# Content-Location: https://ehr.example/fhir/bulkstatus/JOB123
# 2) Poll the status URL until complete
curl -s 'https://ehr.example/fhir/bulkstatus/JOB123' \
-H 'Authorization: Bearer <token>'
# 202 + X-Progress while running; 200 + a manifest JSON when done:
# { "transactionTime": "...", "request": "...", "requiresAccessToken": true,
# "output": [
# { "type": "DocumentReference",
# "url": "https://ehr.example/fhir/bulkfiles/dr-1.ndjson" },
# { "type": "DiagnosticReport",
# "url": "https://ehr.example/fhir/bulkfiles/dx-1.ndjson" } ] }
# 3) Download each NDJSON file (one FHIR resource per line)
curl -s 'https://ehr.example/fhir/bulkfiles/dr-1.ndjson' \
-H 'Authorization: Bearer <token>' -o dr-1.ndjson
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.
- 12d ago First seen · 158 lines · 151 tokens per session scan A 57f188a413e9
exporting-bulk-fhir is a skill published in the GitHub repository maziyarpanahi/openmed (5,302 stars, last pushed today), licensed Apache-2.0. It adds 151 tokens to every session and 1,999 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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