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 StanfordSpezi/SpeziVibe --skill fhir-data-model-designgit clone --depth 1 https://github.com/StanfordSpezi/SpeziVibeWrote 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/stanfordspezi/spezivibe/fhir-data-model-design)<a href="https://agentmods.dev/skills/stanfordspezi/spezivibe/fhir-data-model-design"><img src="https://agentmods.dev/badge/skills/stanfordspezi/spezivibe/fhir-data-model-design/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/stanfordspezi/spezivibe/fhir-data-model-design"><img src="https://agentmods.dev/badge/skills/stanfordspezi/spezivibe/fhir-data-model-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 6 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00032 | $0.04504 |
| Opus 5 | $0.00016 | $0.02252 |
| Sonnet 5 | $0.00006 | $0.00901 |
| Haiku 4.5 | $0.00003 | $0.00450 |
Grade A, and why
fhir-data-model-design 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 11d 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 — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FHIR Data Model Designer
Design a FHIR R4 data model for your digital health app. Ask questions, map clinical concepts to FHIR resources, then produce a structured data model specification document that you and other agents can use to implement data storage, API calls, and TypeScript types — regardless of backend.
Background
FHIR (Fast Healthcare Interoperability Resources) R4 is the standard for healthcare data exchange. It defines a common vocabulary of resource types, terminologies, and API patterns. Getting the data model right from the start avoids painful migrations later.
The key challenge: FHIR has 140+ resource types, dozens of profiles, and many ways to model the same concept. The right choice depends on your clinical use case, interoperability goals, and terminology requirements.
Relationship to health-data-model-planning: that skill decides what the product's data concepts are and whether FHIR is the right lens; this skill turns those concepts into a concrete FHIR R4 specification. If docs/planning/data-model-brief.md exists, read it first — and treat this skill's output as the authoritative FHIR mapping, superseding the brief's preliminary FHIR recommendations.
FHIR conventions used throughout:
- App-level IDs stored in
identifier(notid) — the FHIR server assignsid - Custom code systems:
http://[your-app].com/fhir/CodeSystem/[name] - Custom identifiers:
http://[your-app].com/fhir/identifier/[name] - Standard FHIR fields preferred over extensions; extensions only when no standard field fits
- All API calls use standard FHIR REST:
GET /fhir/[ResourceType]?[params],POST /fhir/[ResourceType]
Your Role
You are an expert FHIR architect. You give concrete recommendations — specific resources, terminology codes, profiles, and sample JSON — based on clinical requirements. You are not Socratic; you provide expert answers.
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.
- 11d ago First seen · 399 lines · 32 tokens per session scan A 042e67c62f57
fhir-data-model-design is a skill published in the GitHub repository StanfordSpezi/SpeziVibe (24 stars, last pushed 3d ago), licensed MIT. It adds 32 tokens to every session and 4,504 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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