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 agentmods add skills/x-cmd/skill/fda-databasenpx skills add x-cmd/skill --skill fda-databasegit clone --depth 1 https://github.com/x-cmd/skillWrote 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/x-cmd/skill/fda-database)<a href="https://agentmods.dev/skills/x-cmd/skill/fda-database"><img src="https://agentmods.dev/badge/skills/x-cmd/skill/fda-database.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00043 | $0.03615 |
| Opus 5 | $0.00022 | $0.01808 |
| Sonnet 5 | $0.00009 | $0.00723 |
| Haiku 4.5 | $0.00004 | $0.00362 |
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 2d 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.
This is a copy
86% identical to fda-database — 6 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.
How it starts
The opening of the file, as written. The whole thing — 518 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
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.
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.
- 2d ago First seen · 518 lines · 43 tokens per session scan A 953adcd5e9ba
fda-database is a skill published in the GitHub repository x-cmd/skill (26 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 3,615 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to fda-database, differing in 6 lines, and is treated as a copy.
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