deidentifying-clinical-text

deidentifying-clinical-text is a skill for Claude Code from maziyarpanahi/openmed. It costs 144 tokens per session (1,909 once invoked), scanned A, original, Apache-2.0.

A local workflow for finding the exact point where a software bug begins. It narrows the search through code paths, commits, data transformations, or isolated components.

In plain words
What is it for?
Investigating crashes, regressions, hard-to-reproduce bugs, faulty components, and unclear data flow.
Why use it?
It reduces a large or unclear debugging problem to a smaller area that can be tested directly.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the openmed-skills plugin — 74 skills shipped together

Good fit Investigating crashes, regressions, hard-to-reproduce bugs, faulty components, and unclear data flow.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/maziyarpanahi/openmed/deidentifying-clinical-text
About the project

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.

maziyarpanahi/openmed · 5,290 stars · on GitHub · openmed.life

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 maziyarpanahi/openmed --skill deidentifying-clinical-text
Clone the repo
git clone --depth 1 https://github.com/maziyarpanahi/openmed

Made for: Claude Code.

Or install openmed-skills, the plugin that ships this one along with the rest of its 74 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.

agentmods badge for deidentifying-clinical-text

README.md
[![agentmods](https://agentmods.dev/badge/skills/maziyarpanahi/openmed/deidentifying-clinical-text/github.svg)](https://agentmods.dev/skills/maziyarpanahi/openmed/deidentifying-clinical-text)
Your own site
<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.

agentmods 80×15 button for deidentifying-clinical-text

Your own site · 80×15
<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>
Per session 144 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,909 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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.

ModelPer sessionOnce 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

Measured 11d ago against content hash b879d35aecb4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

skills/deidentifying-clinical-text/SKILL.md · 144 lines

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)

Read the full file on GitHub · 144 lines

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.

  1. 11d ago First seen · 144 lines · 144 tokens per session scan A b879d35aecb4

Subscribe to this mod's changes

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.

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