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 auditing-safe-harbor-checklistgit 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/auditing-safe-harbor-checklist)<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/auditing-safe-harbor-checklist"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/auditing-safe-harbor-checklist/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/auditing-safe-harbor-checklist"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/auditing-safe-harbor-checklist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00146 | $0.01693 |
| Opus 5 | $0.00073 | $0.00847 |
| Sonnet 5 | $0.00029 | $0.00339 |
| Haiku 4.5 | $0.00015 | $0.00169 |
Grade A, and why
auditing-safe-harbor-checklist 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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auditing against the HIPAA Safe Harbor checklist
The Safe Harbor method (45 CFR 164.514(b)(2)) de-identifies PHI by removing 18 specific identifier categories for the individual and their relatives, employers, and household members — and requires the covered entity to have no actual knowledge that the remainder could re-identify anyone. This skill turns that legal checklist into a concrete coverage check over OpenMed output: which of the 18 categories were detected and handled, and where the gaps are.
The full mapping table lives in
references/safe-harbor-identifiers.md —
all 18 categories, their OpenMed HIPAA class, the matching CANONICAL_LABELS,
and per-category cautions. Read it when you need the authoritative cross-walk.
When to use this skill
Use it after a de-identification run to prove coverage, or before release to
decide whether Safe Harbor is even achievable for this text. If the user needs a
signed, retained record of the run, hand off to auditing-deidentification-runs.
Quick start: coverage check
import openmed
from openmed.core.labels import LABEL_TO_HIPAA, HIPAA_SAFE_HARBOR_CLASSES
note = (
"Patient John Doe (MRN 1234567), age 92, of Smalltown, seen 2024-03-02. "
"SSN 123-45-6789, phone 617-555-0142."
)
# 1) Detect identifiers (spans only; no rewrite).
detected = openmed.extract_pii(note)
# 2) Roll each detected span up to its HIPAA Safe Harbor class.
covered = set()
for ent in detected.entities:
canonical = openmed.normalize_label(ent.label) # -> CANONICAL_LABELS form
hipaa_class = LABEL_TO_HIPAA.get(canonical) # -> one of 18 classes
if hipaa_class:
covered.add(hipaa_class)
# 3) Report which of the 18 classes were touched and which weren't observed.
missing = sorted(HIPAA_SAFE_HARBOR_CLASSES - covered)
print("covered:", sorted(covered))
print("not observed in this note:", missing)
"Not observed" is not the same as "absent" — a category may simply not occur in this note, or may have been missed. That is exactly what the human review step (below) is for.
What ships with it
1 file 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.
- 12d ago First seen · 127 lines · 146 tokens per session scan A 0d0583ce43f6
auditing-safe-harbor-checklist is a skill published in the GitHub repository maziyarpanahi/openmed (5,290 stars, last pushed yesterday), licensed Apache-2.0. It adds 146 tokens to every session and 1,693 once invoked, about $0.0007 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 2026-08-30.
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