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 parsing-lab-valuesgit 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/parsing-lab-values)<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/parsing-lab-values"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/parsing-lab-values/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/parsing-lab-values"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/parsing-lab-values.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 | $0.00142 | $0.01725 |
| Opus 5 | $0.00071 | $0.00863 |
| Sonnet 5 | $0.00028 | $0.00345 |
| Haiku 4.5 | $0.00014 | $0.00172 |
Grade A, and why
parsing-lab-values 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parsing lab values
Lab results in clinical text arrive as a value, a unit, and a reference range
("Sodium 132 mmol/L (135–145)"). To act on them you need a structured
abnormal flag — is 132 low, normal, high, or critical? OpenMed's
openmed.clinical lab helpers parse the reference range deterministically and
derive the flag, honoring any explicit flag the originating lab already supplied.
The helpers are unit-agnostic by design: they compare numbers within a stated
range and never convert units, so a mmol/L value is never silently compared
against a mg/dL range.
When to use
- After
extracting-clinical-entitiessurfaces lab/measurement entities and you need to classify each as low / normal / high / critical. - The user asks to parse reference ranges, flag abnormal labs, build a flagged
labs table, or interpret values like
<5,>=10,0.5 - 1.2. - You have an originating-lab flag (
H,L,C,HH) and want it honored over a derived comparison.
Quick start
from openmed.clinical import (
parse_reference_range, derive_abnormal_flag, LAB_FLAG_ADVISORY,
)
# Closed range
rng = parse_reference_range("135-145")
# -> {"low": 135.0, "high": 145.0, "low_inclusive": True, "high_inclusive": True}
derive_abnormal_flag(132, rng) # "low"
derive_abnormal_flag(140, "135-145") # "normal" (raw range string accepted)
derive_abnormal_flag(150, "135 to 145") # "high"
# One-sided bounds
derive_abnormal_flag(7, parse_reference_range("<5")) # "high" (above the cap)
derive_abnormal_flag(3, parse_reference_range(">=10")) # "low"
# Honor the lab's own explicit flag (takes precedence over derived comparison)
derive_abnormal_flag(132, "135-145", explicit_flag="C") # "critical"
derive_abnormal_flag(132, "135-145", explicit_flag="HH") # "critical"
# Unparseable / non-numeric inputs fail safe rather than guessing
derive_abnormal_flag("pending", "135-145") # "unknown"
derive_abnormal_flag(132, "see report") # "unknown"
print(LAB_FLAG_ADVISORY) # surface this disclaimer with derived flags
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 · 128 lines · 142 tokens per session scan A f1ce68cbab18
parsing-lab-values is a skill published in the GitHub repository maziyarpanahi/openmed (5,290 stars, last pushed today), licensed Apache-2.0. It adds 142 tokens to every session and 1,725 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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