Agent skill · Backend & API

querying-openfda-labels

Looks up FDA drug labels, NDC directory entries, indications, boxed warnings, and recalls/enforcement actions via the free public OpenFDA API to enrich drugs that OpenMed extracts. Use when the user wants the prescribing information for a drug, its boxed warning, approved indications, dosage forms and routes, package NDC codes, RxCUI, or whether a product has an open recall. Trigger keywords: OpenFDA, drug label, SPL, prescribing information, boxed warning, black box warning, indications, NDC, package code, recall, enforcement, Class I recall, drug enrichment. Pairs adjacent to OpenMed NER: ta

maziyarpanahigithub.com/maziyarpanahiGitHub ↗
claude-codeApache-2.0
Install
npx skills add maziyarpanahi/openmed --skill querying-openfda-labels --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 7 KB
Bundled scripts: none
Version: 1.0
Path: skills/querying-openfda-labels/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 4,851
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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_

What's inside
Steps it walks through
  1. When to use
  2. The three endpoints
  3. Quick start (real OpenFDA queries)
  4. Workflow
  5. Hand-off to / from OpenMed
  6. Edge cases & gotchas
  7. Standards & references
More from openmed
All skills →
About this skill
What does the querying-openfda-labels skill do?

Looks up FDA drug labels, NDC directory entries, indications, boxed warnings, and recalls/enforcement actions via the free public OpenFDA API to enrich drugs that OpenMed extracts. Use when the user wants the prescribing information for a drug, its boxed warning, approved indications, dosage forms and routes, package NDC codes, RxCUI, or whether a product has an open recall. Trigger keywords: OpenFDA, drug label, SPL, prescribing information, boxed warning, black box warning, indications, NDC, package code, recall, enforcement, Class I recall, drug enrichment. Pairs adjacent to OpenMed NER: ta

How do I install it?

Run `npx skills add maziyarpanahi/openmed --skill querying-openfda-labels --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From maziyarpanahi/openmed, a repository with 4,851 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going