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 diabetes-panel-reviewgit 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/diabetes-panel-review)<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/diabetes-panel-review"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/diabetes-panel-review/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/diabetes-panel-review"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/diabetes-panel-review.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 | $0.00109 | $0.04081 |
| Opus 5 | $0.00055 | $0.02041 |
| Sonnet 5 | $0.00022 | $0.00816 |
| Haiku 4.5 | $0.00011 | $0.00408 |
Grade A, and why
diabetes-panel-review 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 13d 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 — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diabetes Panel Review
Overview
Pull all diabetes-relevant laboratory Observations: HbA1c, fasting glucose, random glucose, urine microalbumin, urine albumin-to-creatinine ratio, lipid panel, eGFR, creatinine, and serum potassium. Trend HbA1c over time. Classify glycemic control per ADA Standards of Care 2024 targets. Assess complications screening status for retinopathy, nephropathy, and neuropathy. Evaluate cardiovascular risk factors. Generate a structured diabetes management summary with actionable recommendations.
FHIR Resources Used
| Resource | Purpose | Key Fields |
|---|---|---|
| Observation | Lab results (HbA1c, glucose, lipids, renal, urine) | code, valueQuantity, effectiveDateTime, interpretation, referenceRange |
| Condition | Diabetes diagnosis and complications | code, clinicalStatus, onsetDateTime |
| MedicationStatement | Diabetes medications | medicationCodeableConcept, status, dosage |
| Procedure | Screening procedures (eye exam, foot exam) | code, performedDateTime, status |
| Patient | Demographics for target individualization | birthDate, gender |
| CarePlan | Existing diabetes care plans | status, activity, period |
Instructions
Step 1: Confirm Diabetes Diagnosis and Type
Tool: fhir_search
resourceType: "Condition"
queryParams: "patient=[patient-id]&code=http://snomed.info/sct|44054006,http://snomed.info/sct|73211009,http://snomed.info/sct|46635009&clinical-status=active"
SNOMED codes:
- 44054006 = Type 2 Diabetes Mellitus
- 73211009 = Type 1 Diabetes Mellitus
- 46635009 = Type 1 Diabetes Mellitus (alternate)
If no Condition found, check for HbA1c >= 6.5% or diabetes medications as proxy evidence. Note the diagnosis gap.
Extract onsetDateTime to determine disease duration (affects target selection).
Step 2: Retrieve Patient Demographics
Tool: fhir_read
resourceType: "Patient"
id: "[patient-id]"
Extract age. Age determines A1c target individualization:
- Younger adults, short disease duration, no CVD: target < 6.5%
- Most adults: target < 7.0%
- Older adults (> 65), long disease duration, significant comorbidities: target < 8.0%
- Limited life expectancy, extensive comorbidities: avoid symptomatic hyperglycemia, less stringent targets
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.
- 13d ago First seen · 311 lines · 109 tokens per session scan A d657155746f5
diabetes-panel-review is a skill published in the GitHub repository langcare/langcare-mcp-fhir (55 stars, last pushed 5mo ago), licensed MIT. It adds 109 tokens to every session and 4,081 once invoked, about $0.0005 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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