parsing-lab-values

parsing-lab-values is a skill for Claude Code, Codex from maziyarpanahi/openmed. It costs 142 tokens per session (1,725 once invoked), scanned A, original, Apache-2.0.

A clinical-text helper that reads laboratory values and their reference ranges, then labels results as low, normal, high, or critical.

In plain words
What is it for?
Use it to parse ranges such as “135–145” or “<5”, classify extracted lab results, and build tables of flagged measurements.
Why use it?
It turns loosely written lab results into consistent findings without silently comparing values that use different units. It can also respect a warning supplied by the laboratory.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

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

Good fit Use it to parse ranges such as “135–145” or “<5”, classify extracted lab results, and build tables of flagged measurements.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/maziyarpanahi/openmed/parsing-lab-values
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 parsing-lab-values
Clone the repo
git clone --depth 1 https://github.com/maziyarpanahi/openmed

Made for: Claude Code, Codex.

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 parsing-lab-values

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

agentmods 80×15 button for parsing-lab-values

Your own site · 80×15
<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>
Per session 142 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,725 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 $0.00142 $0.01725
Opus 5 $0.00071 $0.00863
Sonnet 5 $0.00028 $0.00345
Haiku 4.5 $0.00014 $0.00172

Measured 11d ago against content hash f1ce68cbab18, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

skills/parsing-lab-values/SKILL.md · 128 lines

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-entities surfaces 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

Read the full file on GitHub · 128 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 · 128 lines · 142 tokens per session scan A f1ce68cbab18

Subscribe to this mod's changes

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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