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 referralsgit 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/referrals)<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/referrals"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/referrals/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/referrals"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/referrals.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 | $0.00058 | $0.00625 |
| Opus 5.5 | $0.00023 | $0.00250 |
| Sonnet 5.5 | $0.00012 | $0.00125 |
| Haiku 4.5 | $0.00006 | $0.00063 |
Grade A, and why
langcare-referrals 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Referral Generator
When to Use This Skill
Use when a clinician needs to create a specialist referral with supporting clinical documentation compiled from the patient's FHIR record.
Clinical Workflow
- Use
fhir_readto retrieve Patient demographics and insurance (Coverage) for referral header - Use
fhir_searchto pull relevant Condition resources for the referral indication - Use
fhir_searchto pull relevant Observation resources (labs, vitals) supporting the referral - Use
fhir_searchto pull active MedicationRequest resources for current treatment context - Use
fhir_searchto pull relevant Procedure resources for prior interventions - Compile referral package: demographics, insurance, referring diagnosis, clinical question, relevant labs/vitals, current medications, prior treatments attempted
- Use
fhir_createto create ServiceRequest resource with category=referral, reasonCode, and supportingInfo references
FHIR Resources
- Patient -- Demographics for referral header
- Coverage -- Insurance for authorization
- Condition -- Referral indication and supporting diagnoses
- Observation -- Relevant lab results and vitals
- MedicationRequest -- Current medications
- Procedure -- Prior related procedures
- ServiceRequest -- Output: referral order
FHIR Query Examples
Create Referral Order
fhir_create(resourceType="ServiceRequest", resource={"resourceType":"ServiceRequest","status":"active","intent":"order","category":[{"coding":[{"system":"http://snomed.info/sct","code":"3457005","display":"Patient referral"}]}],"code":{"text":"[specialty] referral"},"subject":{"reference":"Patient/[patient-id]"},"reasonCode":[{"text":"[referral reason]"}]})
Clinical Guidelines
- CMS referral and prior authorization requirements
- Specialty-specific referral criteria (e.g., nephrology at eGFR <30, cardiology for new murmur/arrhythmia)
Interpretation Guide
- Structure the referral with: reason for referral (clinical question), relevant history, current medications, pertinent labs, prior treatments, specific request (evaluation, co-management, procedure)
- Include insurance authorization requirements if applicable
- Flag urgent referrals: conditions requiring expedited evaluation (suspected malignancy, acute decompensation, new neurologic deficit)
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 · 55 lines · 58 tokens per session scan A 0615dd799d9e
langcare-referrals is a skill published in the GitHub repository langcare/langcare-mcp-fhir (71 stars, last pushed 6mo ago), licensed MIT. It adds 58 tokens to every session and 625 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.
langcare-referrals compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| langcare-referralslangcare/langcare-mcp-fhir | Skill | 71 | 6mo ago | MIT | A | 58 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |