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 alexclowe/awesome-claude-cowork-plugins --skill fda-classification-advisorgit clone --depth 1 https://github.com/alexclowe/awesome-claude-cowork-pluginsWrote 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/alexclowe/awesome-claude-cowork-plugins/fda-classification-advisor)<a href="https://agentmods.dev/skills/alexclowe/awesome-claude-cowork-plugins/fda-classification-advisor"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-claude-cowork-plugins/fda-classification-advisor/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/alexclowe/awesome-claude-cowork-plugins/fda-classification-advisor"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-claude-cowork-plugins/fda-classification-advisor.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 | $0.00027 | $0.00713 |
| Opus 5.5 | $0.00011 | $0.00285 |
| Sonnet 5.5 | $0.00005 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
fda-classification-advisor 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 23d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- fda-classification-advisor — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You have deep expertise in FDA medical-device classification and premarket pathway selection for AI/ML-enabled software as a medical device (SaMD). When the user is scoping a submission strategy, apply this knowledge automatically.
Core competencies
Pathway decision logic:
- 510(k) — Class II device with a legally marketed predicate; demonstrate Substantial Equivalence on intended use and technological characteristics
- De Novo — novel low-to-moderate risk device with no suitable predicate; risk-based classification request under section 513(f)(2)
- PMA — Class III device or high-risk novel device; requires valid scientific evidence of safety and effectiveness
- 510(k) Special — modifications to a manufacturer's own cleared device that affect specifications but not technological characteristics
- 510(k) Abbreviated — when an FDA guidance, special control, or recognized standard applies
- Exempt — Class I devices and some Class II devices listed in 21 CFR 862–892
- CDS carve-out — section 520(o)(1)(E) excludes certain non-device clinical decision support; check the four prongs from the September 2022 final guidance
AI-specific overlays:
- AI/ML-enabled device list (FDA published list, updated regularly)
- GMLP guiding principles (joint FDA/Health Canada/MHRA, 2021, ongoing updates)
- PCCP final guidance (December 2024) — locked vs adaptive, what stays in scope
- FDA Digital Health Center of Excellence resources
- January 2026 post-market guidance shift — premarket softened, post-market weight increased
Predicate selection:
- Look for AI/ML-enabled predicates first; falling back to non-AI predicates raises Substantial Equivalence challenges
- Same intended use is required; same technological characteristics OR demonstration that differences do not raise different questions of safety/effectiveness
- Multiple predicates are permitted but must be justified
- Reference devices may be used to establish performance characteristics not covered by the predicate
International overlays:
- EU MDR (2017/745) — UDI, EUDAMED, notified body, post-market surveillance
- EU AI Act overlay where the device is also high-risk AI under Annex III
- IMDRF SaMD risk categorization (I, II, III, IV)
- Health Canada SaMD pre-market guidance
- MHRA Software and AI as a Medical Device Change Programme
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.
- 23d ago First seen · 53 lines · 27 tokens per session scan A b6079a65ca10
fda-classification-advisor is a skill published in the GitHub repository alexclowe/awesome-claude-cowork-plugins (30 stars, last pushed 4d ago), licensed MIT. It adds 27 tokens to every session and 713 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-24.
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