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 health-data-model-planninggit 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/health-data-model-planning)<a href="https://agentmods.dev/skills/stanfordspezi/spezivibe/health-data-model-planning"><img src="https://agentmods.dev/badge/skills/stanfordspezi/spezivibe/health-data-model-planning/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/health-data-model-planning"><img src="https://agentmods.dev/badge/skills/stanfordspezi/spezivibe/health-data-model-planning.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 | $0.00026 | $0.01386 |
| Opus 5 | $0.00013 | $0.00693 |
| Sonnet 5 | $0.00005 | $0.00277 |
| Haiku 4.5 | $0.00003 | $0.00139 |
Grade A, and why
health-data-model-planning 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 12d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Health Data Model Planner
Plan a health data model with a strong bias toward FHIR before committing to storage, code structures, or client-specific architecture.
When to Use
Use this skill when you need to:
- define the core health concepts a product needs to represent
- decide which entities, relationships, and lifecycle states matter
- reason about interoperability requirements with FHIR as the default starting point for clinical data
- prepare for later implementation in a mobile app, backend, analytics pipeline, or research workflow
Relationship to fhir-data-model-design: this skill plans the concepts — entities, relationships, lifecycle states, and whether FHIR fits. When the app needs a concrete FHIR R4 specification (specific resources, profiles, and terminology bindings), run fhir-data-model-design afterwards; its output supersedes this brief's preliminary FHIR recommendations.
Working Style
Start with the domain, but assume that clinically meaningful data will often benefit from a FHIR-oriented design unless there is a strong reason not to.
Clarify:
- what the product is trying to track or coordinate
- who creates, reads, updates, or interprets the data
- what must be longitudinal, auditable, shareable, or clinically meaningful
- what needs to be structured versus free-form
- which standards, institutions, or downstream systems matter
Default Bias Toward FHIR
In digital health, prefer asking "How would this map to FHIR?" early rather than treating interoperability as a late-stage add-on.
Default questions:
- should this concept become a FHIR resource instead of a custom object
- is there an existing FHIR pattern for this workflow
- should values use established terminologies such as LOINC, SNOMED CT, or RxNorm
- will this data eventually need to move between apps, research systems, backend services, or EHR-adjacent systems
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.
- 12d ago First seen · 179 lines · 26 tokens per session scan A 5eb5cc325088
health-data-model-planning is a skill published in the GitHub repository StanfordSpezi/SpeziVibe (24 stars, last pushed 4d ago), licensed MIT. It adds 26 tokens to every session and 1,386 once invoked, about $0.0001 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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