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 fasten-ehr-integrationgit 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/fasten-ehr-integration)<a href="https://agentmods.dev/skills/stanfordspezi/spezivibe/fasten-ehr-integration"><img src="https://agentmods.dev/badge/skills/stanfordspezi/spezivibe/fasten-ehr-integration/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/fasten-ehr-integration"><img src="https://agentmods.dev/badge/skills/stanfordspezi/spezivibe/fasten-ehr-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
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 →
- medium Data Exfiltration · line 91 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 134 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00049 | $0.05616 |
| Opus 5.5 | $0.00020 | $0.02246 |
| Sonnet 5.5 | $0.00010 | $0.01123 |
| Haiku 4.5 | $0.00005 | $0.00562 |
Grade A, and why
fasten-ehr-integration 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 2mo 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fasten EHR Integration
Walk the user through connecting their app to real electronic health record (EHR) data with Fasten Connect — from developer account to a working, tested integration that downloads a patient's FHIR records into the app's own storage, plus a saved brief of the privacy and architecture decisions (docs/planning/ehr-connection-brief.md). In this architecture the health data ends up in the user's app, and that makes it their responsibility — do not let the integration ship without the privacy walkthrough.
When to Use
Use this skill when the user wants to:
- let patients pull their own medical records into a digital health app
- get real FHIR data (conditions, meds, labs, documents) without per-health-system integrations
- prototype against realistic EHR sandboxes before going live
Do not use this skill when:
- the app must write to an EHR, or clinicians launch it from inside the EHR — that is SMART on FHIR / vendor-marketplace territory
- the data source is wearables or HealthKit — use
health-data-model-planning - synthetic FHIR data is enough — Fasten publishes an example EHI export (Epic sandbox, JSONL), and Synthea or public SMART sandboxes need no account at all
Background
Fasten Connect is a patient-consent record aggregator. Instead of integrating with each EHR vendor and health system individually, the app embeds one widget and the patient authorizes access themselves:
Patient ──logs into their own patient portal──▶ Provider EHR
│ │
▼ ▼
Your app ◀──FHIR records (webhook + download)── Fasten Connect
- The app embeds Fasten's Stitch widget. The patient finds their health system and signs in to their own patient portal (e.g., MyChart). Credentials go to the provider, never to the app.
- The authorized connection comes back as an
org_connection_idthe backend stores — this is the durable consent handle. - The backend requests a bulk EHI export. Fasten retrieves the records and packages them as FHIR resources in NDJSON/JSONL (one resource per line; R4 in practice for the major EHR patient-access APIs).
- Fasten notifies the backend via webhook with download links; the backend downloads the files. Fasten deletes the export from its storage after 24 hours — the app's copy becomes the only copy in this flow.
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.
- 2mo ago First seen · 289 lines · 49 tokens per session scan A 0e32fa5e5a9a
fasten-ehr-integration is a skill published in the GitHub repository StanfordSpezi/SpeziVibe (26 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 5,616 once invoked, about $0.0002 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 2026-08-30.
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