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 reidentifying-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/reidentifying-text)<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/reidentifying-text"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/reidentifying-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/reidentifying-text"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/reidentifying-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 | $0.00131 | $0.01840 |
| Opus 5 | $0.00066 | $0.00920 |
| Sonnet 5 | $0.00026 | $0.00368 |
| Haiku 4.5 | $0.00013 | $0.00184 |
Grade A, and why
reidentifying-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 7d 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 — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reidentifying Text
Some workflows need to remove PHI for processing but keep the ability to
restore it later under authorization — adjudication, patient recontact, linking
results back to a record. That is pseudonymization (reversible), not
anonymization (irreversible). OpenMed supports it with
deidentify(..., keep_mapping=True) to capture a mapping, and reidentify to
restore. Everything runs on-device.
When to use
- You need to re-link redacted output to the original record later.
- You are doing GDPR pseudonymization (Art. 4(5)): identifiers held separately, reversible under controlled conditions.
- A reviewer must spot-check redactions against originals.
Do NOT use reversibility when:
- The goal is HIPAA Safe Harbor anonymization or a true anonymous release —
a re-identification mapping defeats anonymization. Use
method="remove"and keep no mapping. - The redacted text leaves your trust boundary and the mapping might travel with it. The mapping is the secret; never co-locate it with the de-identified output.
Install
pip install "openmed[hf]"
Quick start: reversible round-trip
import openmed
note = "Patient John Doe (MRN 00481726) seen on 2024-03-02 by Dr. Alice Smith."
# 1) De-identify AND capture the reversal mapping
deid = openmed.deidentify(
note,
method="mask", # or "replace" for realistic surrogates
keep_mapping=True, # <-- required to enable reidentify()
policy="gdpr_pseudonymization",
)
safe_text = deid.deidentified_text # ship/process this
mapping = deid.mapping # SECRET: store separately, encrypted
# 2) Later, under authorization, restore the original
restored = openmed.reidentify(safe_text, mapping)
assert restored == note
reidentify(deidentified_text, mapping) performs the inverse substitution. The
mapping is a dict[str, str] of redacted → original text, produced only when
keep_mapping=True.
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.
- 7d ago First seen · 175 lines · 131 tokens per session scan A 2a43740fa73f
reidentifying-text is a skill published in the GitHub repository maziyarpanahi/openmed (5,290 stars, last pushed today), licensed Apache-2.0. It adds 131 tokens to every session and 1,840 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 2026-09-03.
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.