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 sepsis-qsofagit 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/sepsis-qsofa)<a href="https://agentmods.dev/skills/langcare/langcare-mcp-fhir/sepsis-qsofa"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/sepsis-qsofa/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/sepsis-qsofa"><img src="https://agentmods.dev/badge/skills/langcare/langcare-mcp-fhir/sepsis-qsofa.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 | $0.00090 | $0.01021 |
| Opus 5.5 | $0.00036 | $0.00408 |
| Sonnet 5.5 | $0.00018 | $0.00204 |
| Haiku 4.5 | $0.00009 | $0.00102 |
Grade A, and why
langcare-sepsis-qsofa 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sepsis Screening (qSOFA/SOFA/SIRS)
When to Use This Skill
Use when a clinician needs to calculate sepsis risk scores from vital signs and laboratory data, with bundle compliance assessment.
Clinical Workflow
- Use
fhir_readto retrieve Patient demographics (age for baseline risk) - Use
fhir_searchto pull vital signs (last 24h): temperature (8310-5), heart rate (8867-4), respiratory rate (9279-1), systolic BP (8480-6), SpO2 (2708-6) - Use
fhir_searchto pull labs: WBC (6690-2), lactate (2524-7), creatinine (2160-0), bilirubin (1975-2), platelets (777-3), PaO2 (2703-7), FiO2 (3150-0) - Use
fhir_searchto pull active Condition resources for infection source identification - Use
fhir_searchto pull active MedicationRequest for vasopressor status and current antibiotics - Calculate qSOFA (0-3), SIRS (0-4), and SOFA (0-24) scores (see references/qsofa-sofa-scoring.md)
- Evaluate hour-1 bundle compliance: lactate measured, blood cultures ordered, antibiotics administered, IV fluids initiated, vasopressors if MAP <65
- Use
fhir_createto create ClinicalImpression resource documenting the assessment
FHIR Resources
- Observation -- Vitals (LOINC codes for temp, HR, RR, BP, SpO2) and labs (WBC, lactate, Cr, bilirubin, platelets, PaO2/FiO2)
- Condition -- Active infection source identification
- MedicationRequest -- Vasopressors (norepinephrine, epinephrine, vasopressin, dopamine) and antibiotics
- Procedure -- Mechanical ventilation status (SNOMED 40617009)
- ServiceRequest -- Blood culture orders (LOINC 600-7)
- ClinicalImpression -- Output: sepsis risk documentation
FHIR Query Examples
Pull Recent Vitals
fhir_search(resourceType="Observation", queryParams="patient=[patient-id]&code=8310-5&_sort=-date&_count=5")
Pull Lactate
fhir_search(resourceType="Observation", queryParams="patient=[patient-id]&code=2524-7,32693-4,2519-7&_sort=-date&_count=3")
Check Vasopressor Orders
fhir_search(resourceType="MedicationRequest", queryParams="patient=[patient-id]&status=active&code=3628,3616,11149,35208")
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 · 68 lines · 90 tokens per session scan A dd156e94eac0
langcare-sepsis-qsofa is a skill published in the GitHub repository langcare/langcare-mcp-fhir (71 stars, last pushed 6mo ago), licensed MIT. It adds 90 tokens to every session and 1,021 once invoked, about $0.0004 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-sepsis-qsofa compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| langcare-sepsis-qsofalangcare/langcare-mcp-fhir | Skill | 71 | 6mo ago | MIT | A | 90 | 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 |