fda-database

fda-database is a skill for Claude Code, Codex from silverstein/claude-scientific-skills-desktop. It costs 43 tokens per session (3,441 once invoked), scanned A, a copy of fda-database, MIT.

A skill for querying openFDA, the U.S. Food and Drug Administration’s public API, for regulatory and safety data about medicines, medical devices, food, veterinary products, and chemical substances.

In plain words
What is it for?
Use it to research adverse events, product labels, approvals, recalls, drug shortages, device classifications and clearances, National Drug Codes, and UNII substance identifiers.
Why use it?
It avoids manually searching separate FDA datasets and gives research workflows a consistent way to retrieve related records. It supports investigations into safety issues, approvals, recalls, and supply problems.

Skill for Claude CodeCodex

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

Good fit Use it to research adverse events, product labels, approvals, recalls, drug shortages, device classifications and clearances, National Drug Codes, and UNII substance identifiers.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/silverstein/claude-scientific-skills-desktop/fda-database
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 silverstein/claude-scientific-skills-desktop --skill fda-database
Clone the repo
git clone --depth 1 https://github.com/silverstein/claude-scientific-skills-desktop

Made for: Claude Code, Codex.

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 fda-database

README.md
[![agentmods](https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/fda-database/github.svg)](https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/fda-database)
Your own site
<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/fda-database"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/fda-database/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 fda-database

Your own site · 80×15
<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/fda-database"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/fda-database.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,441 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.
Origin 97% copy Near-identical to another mod 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 $0.00043 $0.03441
Opus 5 $0.00022 $0.01721
Sonnet 5 $0.00009 $0.00688
Haiku 4.5 $0.00004 $0.00344

Measured 10d ago against content hash 4c3df4a8a16f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

fda-database 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 10d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/fda_examples.py, scripts/fda_query.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

97% identical to fda-database — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

corpus/fda-database/SKILL.md · 513 lines

How it starts

The opening of the file, as written. The whole thing — 513 lines — stays where its author put it; the contents beside it link to each section on GitHub.

FDA Database Access

Overview

Access comprehensive FDA regulatory data through openFDA, the FDA's initiative to provide open APIs for public datasets. Query information about drugs, medical devices, foods, animal/veterinary products, and substances using Python with standardized interfaces.

Key capabilities:

  • Query adverse events for drugs, devices, foods, and veterinary products
  • Access product labeling, approvals, and regulatory submissions
  • Monitor recalls and enforcement actions
  • Look up National Drug Codes (NDC) and substance identifiers (UNII)
  • Analyze device classifications and clearances (510k, PMA)
  • Track drug shortages and supply issues
  • Research chemical structures and substance relationships

When to Use This Skill

This skill should be used when working with:

  • Drug research: Safety profiles, adverse events, labeling, approvals, shortages
  • Medical device surveillance: Adverse events, recalls, 510(k) clearances, PMA approvals
  • Food safety: Recalls, allergen tracking, adverse events, dietary supplements
  • Veterinary medicine: Animal drug adverse events by species and breed
  • Chemical/substance data: UNII lookup, CAS number mapping, molecular structures
  • Regulatory analysis: Approval pathways, enforcement actions, compliance tracking
  • Pharmacovigilance: Post-market surveillance, safety signal detection
  • Scientific research: Drug interactions, comparative safety, epidemiological studies

Quick Start

1. Basic Setup

from scripts.fda_query import FDAQuery

# Initialize (API key optional but recommended)
fda = FDAQuery(api_key="YOUR_API_KEY")

# Query drug adverse events
events = fda.query_drug_events("aspirin", limit=100)

# Get drug labeling
label = fda.query_drug_label("Lipitor", brand=True)

# Search device recalls
recalls = fda.query("device", "enforcement",
                   search="classification:Class+I",
                   limit=50)

2. API Key Setup

While the API works without a key, registering provides higher rate limits:

  • Without key: 240 requests/min, 1,000/day
  • With key: 240 requests/min, 120,000/day

Read the full file on GitHub · 513 lines

Files

What ships with it

8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 10d ago First seen · 513 lines · 43 tokens per session scan A 4c3df4a8a16f

Subscribe to this mod's changes

fda-database is a skill published in the GitHub repository silverstein/claude-scientific-skills-desktop (22 stars, last pushed 5mo ago), licensed MIT. It adds 43 tokens to every session and 3,441 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to fda-database, differing in 7 lines, and is treated as a copy.

Related

Other skills, from other repositories

bio-ortholog-inference

Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when…

PKU-YuanGroup/OpenAI4S · 141 tokens

admet_genetic

ADMET-guided genetic molecule optimization workflow from seed SMILES; use when the agent needs to build or run an RDKit/SA-Score/ADMET-AI GA pipeline for molecule optimization, enforce molecule lineage logs, render optimization-history HTML dashboards, and write candidate triage reports.

PKU-YuanGroup/OpenAI4S · 63 tokens

bioprobench

Score an LLM's biological-protocol reasoning on the BioProBench benchmark: protocol QA, step ordering, error detection, protocol generation, and LLM-judged error reasoning; or generate the responses.

PKU-YuanGroup/OpenAI4S · 46 tokens

alphafold2

Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency…

PKU-YuanGroup/OpenAI4S · 117 tokens

bio-alignment-msa-parsing

Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.

PKU-YuanGroup/OpenAI4S · 52 tokens

bio-causal-genomics-heritability-partitioning

Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML…

PKU-YuanGroup/OpenAI4S · 186 tokens