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 fall-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/fall-risk-assessment)<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/fall-risk-assessment"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/fall-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/fall-risk-assessment"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/fall-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 | $0.00102 | $0.03565 |
| Opus 5 | $0.00051 | $0.01783 |
| Sonnet 5 | $0.00020 | $0.00713 |
| Haiku 4.5 | $0.00010 | $0.00357 |
Grade A, and why
fall-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 — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fall Risk Assessment
Overview
Calculate fall risk scores from FHIR Patient, Condition, Observation, and MedicationRequest resources using Morse Fall Scale, Hendrich II Fall Risk Model, and Timed Up and Go (TUG) test. Identify high-risk medications, mobility deficits, and environmental risk factors. Generate a CarePlan FHIR resource with individualized fall prevention interventions.
FHIR Resources Used
| Resource | Purpose | Key Fields |
|---|---|---|
| Patient | Age, demographics | birthDate, gender |
| Condition | Fall history, diagnoses affecting balance/gait | code, clinicalStatus, onsetDateTime |
| Observation | Vitals (orthostatic BP), mobility assessments, vision | code, valueQuantity |
| MedicationRequest | High-risk medications (sedatives, opioids, antihypertensives) | medicationCodeableConcept, status |
| Procedure | Assistive device use, recent surgeries | code, status |
| CarePlan | Output: fall prevention plan | status, category, activity, goal |
Instructions
Step 1: Retrieve Patient Demographics
Tool: fhir_read
resourceType: "Patient"
id: "[patient-id]"
Extract age and gender. Age >=65 is a primary fall risk factor.
Step 2: Retrieve Fall History and Relevant Conditions
Tool: fhir_search
resourceType: "Condition"
queryParams: "patient=[patient-id]&clinical-status=active,recurrence,remission"
Key SNOMED codes:
- 161898004: Falls (history of falling)
- 217082002: Accidental fall
- 129839007: At risk for falls
- 44695005: Paralysis
- 22253000: Pain (chronic pain affecting mobility)
- 386807006: Impaired memory / cognitive impairment
- 40917007: Visual impairment
- 15188001: Hearing loss
- 85600001: Peripheral neuropathy
- 69896004: Rheumatoid arthritis
- 396275006: Osteoarthritis
- 64859006: Osteoporosis
- 230690007: Stroke (with residual deficit)
- 49049000: Parkinson disease
- 26929004: Alzheimer disease
- 84757009: Epilepsy / seizure disorder
- 271327008: Syncope
- 111516008: Urinary incontinence (urgency increases fall risk)
- 73211009: Diabetes mellitus (neuropathy risk)
- 38341003: Hypertension (orthostatic hypotension risk)
- 35489007: Depressive disorder
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 · 344 lines · 102 tokens per session scan A 930e5122b836
fall-risk-assessment is a skill published in the GitHub repository langcare/langcare-mcp-fhir (55 stars, last pushed 5mo ago), licensed MIT. It adds 102 tokens to every session and 3,565 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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