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 deidentifying-clinical-textgit 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/deidentifying-clinical-text)<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/deidentifying-clinical-text"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/deidentifying-clinical-text/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/deidentifying-clinical-text"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/deidentifying-clinical-text.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 | $0.00144 | $0.01909 |
| Opus 5 | $0.00072 | $0.00955 |
| Sonnet 5 | $0.00029 | $0.00382 |
| Haiku 4.5 | $0.00014 | $0.00191 |
Grade A, and why
deidentifying-clinical-text 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 11d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
De-identifying clinical text
openmed.deidentify detects PHI/PII and rewrites the text so it can be shared,
stored, or analyzed without exposing patients. It runs fully on-device after
a one-time model download — no network calls, no telemetry, no raw PHI leaving
the process. This is the single most important OpenMed entry point for privacy
work; everything else (policies, audit, multilingual, date-shifting) layers on
top of it.
When to use this skill
Reach for deidentify when you need to transform text — replace, mask, remove,
hash, or date-shift the identifiers. If you only need to locate PHI spans
without changing the text, use extract_pii (see extracting-pii-entities). To
restore masked text later, use reidentify (see reidentifying-text).
Quick start
import openmed
note = (
"Patient John Doe (MRN 1234567) was seen on 2024-03-02 by Dr. Alice Reed. "
"Contact: [email protected], 617-555-0142."
)
result = openmed.deidentify(
note,
method="mask", # mask | remove | replace | hash | shift_dates
confidence_threshold=0.7, # safety default; raise to reduce false negatives' impact
policy="hipaa_safe_harbor", # optional bundled profile (see below)
)
print(result.deidentified_text)
# Patient [NAME] (MRN [ID_NUM]) was seen on [DATE] by Dr. [NAME]. ...
for e in result.pii_entities:
# NEVER log e.text / e.original_text — those are raw PHI. Use offsets + label.
print(e.canonical_label, e.start, e.end, round(e.confidence, 3))
deidentify returns a DeidentificationResult with these fields (note the
exact names):
| Field | What it holds |
|---|---|
.deidentified_text |
the rewritten, PHI-safe string (your output) |
.pii_entities |
list[PIIEntity] — each has start, end, canonical_label, confidence, action, surrogate; original_text/text hold raw PHI |
.mapping |
redacted→original dict, only when keep_mapping=True (secret) |
.method |
the method actually applied |
.metadata |
run metadata (model, policy, counts) |
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.
- 11d ago First seen · 144 lines · 144 tokens per session scan A b879d35aecb4
deidentifying-clinical-text is a skill published in the GitHub repository maziyarpanahi/openmed (5,290 stars, last pushed today), licensed Apache-2.0. It adds 144 tokens to every session and 1,909 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.
Other skills, from other repositories
benchmarking
Use this skill when the user wants to benchmark an MLX-VLM change and present the numbers in a PR — fork-vs-main A/B comparisons, isolated-module micro-benchmarks, median-of-N timing with warmup, peak-memory reporting, correctness checks, parameter sweeps, and self-contained reproducible bench scripts to paste into a…
drug-discovery
Drug discovery: ChEMBL search, drug-likeness, interactions.
server-inference
Use this skill when the user wants to run or debug MLX-VLM server inference, including uv run mlxvlm.server, /v1/models, /v1/chat/completions, /v1/responses, streaming, OpenAI-compatible clients, health checks, metrics, model unload/reload, adapters, trust-remote-code, and server request/response failures.
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
molecular-cloning
Molecular cloning simulation and design. PCR amplicon prediction, restriction enzyme digestion, Golden Gate and Gibson assembly simulation, primer design, CRISPR sgRNA design, and plasmid annotation. For protein-level sequence analysis use biopython or esm; for database lookups use gene-database or ensembl-database.