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 beers-criteriagit 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/beers-criteria)<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/beers-criteria"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/beers-criteria/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/beers-criteria"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/beers-criteria.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 | $0.00078 | $0.00697 |
| Opus 5.5 | $0.00031 | $0.00279 |
| Sonnet 5.5 | $0.00016 | $0.00139 |
| Haiku 4.5 | $0.00008 | $0.00070 |
Grade A, and why
langcare-beers-criteria 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Beers Criteria Medication Review
When to Use This Skill
Use when reviewing medications for patients age 65 and older to identify potentially inappropriate prescriptions per AGS Beers Criteria 2023.
Clinical Workflow
- Use
fhir_readto confirm patient age >= 65 from Patient.birthDate - Use
fhir_searchto pull all active MedicationRequest resources - Use
fhir_searchto pull active Condition resources for drug-disease interaction screening - Use
fhir_searchto pull recent Observation resources (renal function for dose adjustments) - Screen each medication against Beers Criteria categories: medications to avoid regardless of diagnosis, medications to avoid with specific conditions, medications to use with caution, drug-drug interactions to avoid, dose adjustments based on renal function
- Present findings with Beers category, quality of evidence, strength of recommendation, and safer alternatives
FHIR Resources
- Patient -- Age verification (must be >= 65)
- MedicationRequest -- Active medications to screen
- Condition -- Active diagnoses for drug-disease interaction screening
- Observation -- Renal function (eGFR/CrCl) for dose-based criteria
FHIR Query Examples
Pull Active Medications
fhir_search(resourceType="MedicationRequest", queryParams="patient=[patient-id]&status=active&_count=100")
Pull Active Conditions
fhir_search(resourceType="Condition", queryParams="patient=[patient-id]&clinical-status=active")
Pull Renal Function
fhir_search(resourceType="Observation", queryParams="patient=[patient-id]&code=33914-3&_sort=-date&_count=1")
Clinical Guidelines
- AGS Beers Criteria 2023 (American Geriatrics Society)
- STOPP/START Criteria v2 (Screening Tool of Older Persons' Prescriptions / Screening Tool to Alert to Right Treatment)
- CMS Part D medication therapy management requirements
Interpretation Guide
- Classify findings by Beers category: Table 2 (avoid regardless), Table 3 (avoid with specific conditions), Table 4 (use with caution), Table 5 (drug-drug interactions), Table 6 (dose adjustment for renal function)
- Rate evidence quality: High, Moderate, Low, Very Low
- Rate recommendation strength: Strong Avoid, Conditional Avoid, Use with Caution
- For each flagged medication, provide: the Beers concern, the clinical rationale, safer alternative options, and whether discontinuation requires a taper
What ships with it
1 file 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.
- 2mo ago First seen · 64 lines · 78 tokens per session scan A 8c6a15813e49
langcare-beers-criteria is a skill published in the GitHub repository langcare/langcare-mcp-fhir (71 stars, last pushed 6mo ago), licensed MIT. It adds 78 tokens to every session and 697 once invoked, about $0.0003 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-beers-criteria compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| langcare-beers-criterialangcare/langcare-mcp-fhir | Skill | 71 | 6mo ago | MIT | A | 78 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |