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 skills add usathyan/epistract --skill fda-product-labelsgit clone --depth 1 https://github.com/usathyan/epistractWrote 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/usathyan/epistract/fda-product-labels)<a href="https://agentmods.dev/skills/usathyan/epistract/fda-product-labels"><img src="https://agentmods.dev/badge/skills/usathyan/epistract/fda-product-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.
<a href="https://agentmods.dev/skills/usathyan/epistract/fda-product-labels"><img src="https://agentmods.dev/badge/skills/usathyan/epistract/fda-product-labels.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.01601 |
| Opus 5 | $0.00000 | $0.00800 |
| Sonnet 5 | $0.00000 | $0.00320 |
| Haiku 4.5 | $0.00000 | $0.00160 |
Grade A, and why
fda-product-labels 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.
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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
fda-product-labels Domain
You are analyzing FDA Structured Product Labeling (SPL) documents — the authoritative regulatory labels submitted by manufacturers for prescription and over-the-counter drug products. Each document is a JSON file containing clinical sections (indications, contraindications, warnings, adverse reactions, pharmacology) alongside structured openfda metadata (brand/generic names, NDC codes, pharmacologic class, manufacturer). Extract precise, clinically meaningful entities and relationships that would support pharmacovigilance, formulary analysis, drug interaction screening, and regulatory intelligence.
Entity Types
| Type | Description |
|---|---|
| DRUG_PRODUCT | A drug product identified by brand/generic name, product type (Rx/OTC), dosage form, and route of administration |
| ACTIVE_INGREDIENT | An active pharmaceutical ingredient (API) with substance name and UNII code |
| INACTIVE_INGREDIENT | An excipient or inactive ingredient in the formulation |
| MANUFACTURER | The manufacturer, labeler, or marketing company responsible for the drug product |
| INDICATION | An FDA-approved therapeutic use or clinical condition the drug is indicated to treat |
| CONTRAINDICATION | A patient condition, prior history, or co-medication that prohibits use of the drug |
| ADVERSE_REACTION | An adverse event or side effect observed in clinical trials or post-marketing surveillance |
| WARNING | A boxed warning, warnings-and-precautions entry, or safety signal requiring clinician action |
| DRUG_INTERACTION | A clinically significant interaction between this drug and another drug, substance, or food that affects safety or efficacy |
| DOSAGE_REGIMEN | A specific dosing instruction including dose amount, frequency, route, and target patient population |
| PATIENT_POPULATION | A patient subgroup with special dosing or safety considerations (pediatric, geriatric, renally impaired, pregnant, nursing mothers) |
| MECHANISM_OF_ACTION | The pharmacological mechanism by which the active ingredient produces its therapeutic effect |
| PHARMACOKINETIC_PROPERTY | A PK parameter including absorption, distribution, metabolism, excretion, half-life, Cmax, bioavailability, or protein binding |
| CLINICAL_STUDY | A clinical trial or study referenced in the label to support safety or efficacy claims |
| PHARMACOLOGIC_CLASS | An FDA-recognized drug class designation (EPC, MoA, Chemical Structure, or PE classification) |
| REGULATORY_IDENTIFIER | A regulatory code such as NDA/ANDA application number, NDC code, RxCUI, UNII, or SPL set ID |
| LABTEST | A laboratory monitoring test referenced in the label for tracking drug safety or efficacy (e.g. liver function tests for hepatotoxic drugs, CBC for immunosuppressants, lipid panels for statins, INR for anticoagulants) |
What ships with it
7 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.
- 10d ago First seen · 67 lines · 0 tokens per session scan A 35cb5ed2a514
fda-product-labels is a skill published in the GitHub repository usathyan/epistract (8 stars, last pushed 25d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,601 tokens. 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 2026-08-31.
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