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 langcare/langcare-mcp-fhir --skill cardiovascular-risk-assessmentgit clone --depth 1 https://github.com/langcare/langcare-mcp-fhirWrote 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/langcare/langcare-mcp-fhir/cardiovascular-risk-assessment)<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/cardiovascular-risk-assessment"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/cardiovascular-risk-assessment/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/langcare/langcare-mcp-fhir/cardiovascular-risk-assessment"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/cardiovascular-risk-assessment.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 | $0.00124 | $0.03170 |
| Opus 5 | $0.00062 | $0.01585 |
| Sonnet 5 | $0.00025 | $0.00634 |
| Haiku 4.5 | $0.00012 | $0.00317 |
Grade A, and why
cardiovascular-risk-assessment 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cardiovascular Risk Assessment
Overview
Calculate multiple cardiovascular risk scores from FHIR Patient, Observation, Condition, and MedicationRequest resources. Supports CHA2DS2-VASc (stroke risk in atrial fibrillation), HEART Score (acute chest pain evaluation), Framingham Risk Score (10-year CHD risk), ASCVD Pooled Cohort Equations (10-year ASCVD risk), and HAS-BLED (bleeding risk on anticoagulation). Generate a RiskAssessment FHIR resource with findings and treatment-threshold recommendations.
FHIR Resources Used
| Resource | Purpose | Key Fields |
|---|---|---|
| Patient | Age, gender, race | birthDate, gender, extension (us-core-race) |
| Condition | CHF, HTN, DM, stroke, vascular disease, AF | code, clinicalStatus |
| Observation | BP, cholesterol, HDL, glucose, HbA1c, INR | code, valueQuantity, effectiveDateTime |
| MedicationRequest | Antiplatelets, anticoagulants, NSAIDs, antihypertensives | medicationCodeableConcept, status |
| RiskAssessment | Output: risk scores | method, prediction, basis |
Instructions
Step 1: Retrieve Patient Demographics
Tool: fhir_read
resourceType: "Patient"
id: "[patient-id]"
Extract: age (from birthDate), gender, race (from US Core extension for ASCVD PCE).
Step 2: Retrieve Active Conditions
Tool: fhir_search
resourceType: "Condition"
queryParams: "patient=[patient-id]&clinical-status=active"
Identify conditions relevant to scoring. Key SNOMED codes:
- 49436004: Atrial fibrillation
- 42343007: Congestive heart failure
- 38341003: Hypertension
- 73211009: Diabetes mellitus
- 230690007: Cerebrovascular accident (stroke)
- 266257000: Transient ischemic attack (TIA)
- 27550009: Vascular disease (peripheral, aortic plaque, prior MI)
- 22298006: Myocardial infarction
- 400047006: Peripheral vascular disease
- 235856003: Hepatic disease
- 709044004: Chronic kidney disease
- 414545008: Ischemic heart disease
Also search historical conditions for stroke/TIA/MI history:
Tool: fhir_search
resourceType: "Condition"
queryParams: "patient=[patient-id]&code=230690007,266257000,22298006"
What ships with it
2 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.
- 12d ago First seen · 314 lines · 124 tokens per session scan A 959d7460960e
cardiovascular-risk-assessment is a skill published in the GitHub repository langcare/langcare-mcp-fhir (55 stars, last pushed 5mo ago), licensed MIT. It adds 124 tokens to every session and 3,170 once invoked, about $0.0006 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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