querying-openfda-labels

querying-openfda-labels is a skill for Claude Code, Codex from maziyarpanahi/openmed. It costs 189 tokens per session (2,009 once invoked), scanned A, original, Apache-2.0.

A lookup skill for OpenFDA, the U.S. Food and Drug Administration's public drug database and API. It retrieves drug labels, National Drug Code package entries, approved uses, boxed warnings, dosage details, routes, and recall information.

In plain words
What is it for?
Use it to look up prescribing information, boxed warnings, indications, dosage forms and routes, package codes, recalls, generic ingredients, or RxCUI mappings for extracted drugs.
Why use it?
It adds authoritative product and regulatory facts after OpenMed has identified a drug in clinical text. It informs human review but does not provide prescribing or treatment advice.

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 look up prescribing information, boxed warnings, indications, dosage forms and routes, package codes, recalls, generic ingredients, or RxCUI mappings for extracted drugs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/maziyarpanahi/openmed/querying-openfda-labels
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 querying-openfda-labels
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 querying-openfda-labels

README.md
[![agentmods](https://agentmods.dev/badge/skills/maziyarpanahi/openmed/querying-openfda-labels/github.svg)](https://agentmods.dev/skills/maziyarpanahi/openmed/querying-openfda-labels)
Your own site
<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/querying-openfda-labels"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/querying-openfda-labels/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 querying-openfda-labels

Your own site · 80×15
<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/querying-openfda-labels"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/querying-openfda-labels.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 189 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,009 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 4 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Data Exfiltration · line 37
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 38
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 39
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
  • medium Data Exfiltration · line 50
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 $0.00189 $0.02009
Opus 5 $0.00095 $0.01005
Sonnet 5 $0.00038 $0.00402
Haiku 4.5 $0.00019 $0.00201

Measured 11d ago against content hash 2ee2fd3f6bd3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

querying-openfda-labels scanned grade A with 1 finding 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

r = requests.get(url, params={"search": search, "limit": limit}, timeout=30)
skills/querying-openfda-labels/SKILL.md · 136 lines

How it starts

The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Querying OpenFDA drug labels, NDC, and recalls

Once OpenMed has pulled a drug name out of a note, you often need authoritative product facts: the boxed warning, approved indications, dosage form / route, package NDC codes, and whether the product is under recall. The FDA's OpenFDA API exposes the Structured Product Labeling (SPL), the NDC directory, and enforcement (recall) reports — all public and free.

This skill is enrichment: it attaches regulatory facts to an extracted drug. It is not clinical decision support — a label lookup informs a human, it does not prescribe.

When to use

  • You extracted a drug and need its boxed warning or indications for display, alerting, or expectedness checks.
  • You need NDC package codes, dosage form, or route for a product.
  • You want to know if a drug/lot is under an open recall (enforcement).
  • You want to map a brand name to its generic ingredient and RxCUI via the label's openfda block.

The three endpoints

Endpoint Use Key fields
https://api.fda.gov/drug/label.json SPL prescribing info boxed_warning, indications_and_usage, warnings, dosage_and_administration, openfda.brand_name, openfda.generic_name, openfda.rxcui, openfda.product_ndc
https://api.fda.gov/drug/ndc.json NDC directory product_ndc, generic_name, brand_name, dosage_form, route, active_ingredients
https://api.fda.gov/drug/enforcement.json Recalls product_description, reason_for_recall, classification (Class I/II/III), recalling_firm, status, recall_initiation_date

No key needed to try it (240 req/min, 1,000/day per IP). A free api_key= raises the daily cap to 120,000.

Quick start (real OpenFDA queries)

import requests

def openfda(endpoint: str, search: str, limit: int = 1) -> list[dict]:
    url = f"https://api.fda.gov/drug/{endpoint}.json"
    r = requests.get(url, params={"search": search, "limit": limit}, timeout=30)
    if r.status_code == 404:        # OpenFDA returns 404 for zero matches
        return []
    r.raise_for_status()
    return r.json().get("results", [])

# 1) Label: boxed warning + indications for a generic drug.
label = openfda("label", 'openfda.generic_name:"warfarin"')
if label:
    rec = label[0]
    print("Boxed warning:", rec.get("boxed_warning", ["(none)"])[0][:200])
    print("Indication:", rec.get("indications_and_usage", ["(none)"])[0][:200])
    print("RxCUI:", rec.get("openfda", {}).get("rxcui"))

# 2) NDC: package codes, form, route.
ndc = openfda("ndc", 'generic_name:"warfarin"', limit=5)
for rec in ndc:
    print(rec["product_ndc"], rec.get("dosage_form"), rec.get("route"))

# 3) Enforcement: open recalls for a product.
recalls = openfda("enforcement",
                  'product_description:"warfarin"+AND+status:"Ongoing"', limit=5)
for rec in recalls:
    print(rec["classification"], "-", rec["reason_for_recall"][:120])

Read the full file on GitHub · 136 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 · 136 lines · 189 tokens per session scan A 2ee2fd3f6bd3

Subscribe to this mod's changes

querying-openfda-labels is a skill published in the GitHub repository maziyarpanahi/openmed (5,290 stars, last pushed today), licensed Apache-2.0. It adds 189 tokens to every session and 2,009 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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