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 opioid-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/opioid-risk-assessment)<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/opioid-risk-assessment"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/opioid-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/opioid-risk-assessment"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/opioid-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 | $0.00108 | $0.04114 |
| Opus 5 | $0.00054 | $0.02057 |
| Sonnet 5 | $0.00022 | $0.00823 |
| Haiku 4.5 | $0.00011 | $0.00411 |
Grade A, and why
opioid-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 9d 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 — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Opioid Risk Assessment
Overview
Perform a comprehensive opioid risk assessment by pulling all active opioid prescriptions, calculating total daily morphine milligram equivalents (MME), applying Opioid Risk Tool (ORT) scoring criteria, and evaluating against CDC 2022 Clinical Practice Guideline thresholds. Flag MME >50 (caution), MME >90 (high risk), concurrent benzodiazepine use, concurrent gabapentinoid use, and absence of naloxone co-prescription. Include CAGE-AID screening questions for substance use risk assessment.
FHIR Resources Used
| Resource | Purpose | Key Fields |
|---|---|---|
| MedicationRequest | Active opioid prescriptions with dosing | status, medicationCodeableConcept, dosageInstruction, dispenseRequest |
| MedicationStatement | Patient-reported opioid and concurrent substance use | status, medicationCodeableConcept, dosage |
| MedicationDispense | Opioid fill history for pattern analysis | status, medicationCodeableConcept, quantity, daysSupply, whenHandedOver |
| Patient | Demographics for ORT scoring (age, gender) | birthDate, gender |
| Condition | Psychiatric diagnoses, substance use history for ORT | code, clinicalStatus |
| Observation | Urine drug screen results, pain scores | code, valueCodeableConcept, valueQuantity |
Instructions
Step 1: Retrieve Patient Demographics
Tool: fhir_read
resourceType: "Patient"
id: "[patient-id]"
Extract age and gender. Both are required for ORT scoring (scoring criteria differ by gender).
Step 2: Pull All Active Medications
Tool: fhir_search
resourceType: "MedicationRequest"
queryParams: "patient=[patient-id]&status=active&_count=100"
Classify each active medication:
- Opioids: Identify by RxNorm code or drug name (see references/mme-conversion-table.md)
- Benzodiazepines: alprazolam, clonazepam, diazepam, lorazepam, temazepam, triazolam, chlordiazepoxide, clorazepate, oxazepam, midazolam
- Gabapentinoids: gabapentin, pregabalin
- Z-drugs: zolpidem, zaleplon, eszopiclone
- Muscle relaxants: carisoprodol, cyclobenzaprine, methocarbamol, tizanidine
- Naloxone: Check if co-prescribed (nasal naloxone, injectable naloxone)
What ships with it
3 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.
- 9d ago First seen · 370 lines · 108 tokens per session scan A 535501ac642c
opioid-risk-assessment is a skill published in the GitHub repository langcare/langcare-mcp-fhir (55 stars, last pushed 5mo ago), licensed MIT. It adds 108 tokens to every session and 4,114 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 2026-09-03.
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