Install any skill in seconds. Free to start, no credit card required.
Get Started Free →FHIR (Fast Healthcare Interoperability Resources) standard. Tools for reading, writing, and querying healthcare data via FHIR APIs: patients, observations, conditions, medications, procedures. Interop with EHR systems.
.claude/skills/mkurman-fhir/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -18% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -17% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -26% | 0% |
| case-04 | ✓→✗ | ▼ Worse | -9% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 26% | 0% |
FHIR (Fast Healthcare Interoperability Resources) tools for reading, writing, and querying healthcare data via FHIR REST APIs. Work with Patient, Observation, Condition, MedicationRequest, and Encounter resources from EHR systems.
bashuv pip install fhir.resources requests
pythonimport requests from fhir.resources.patient import Patient base_url = "https://hapi.fhir.org/baseR4" resp = requests.get(f"{base_url}/Patient", params={"family": "Smith", "birthdate": "gt1970"}) data = resp.json() for entry in data.get("entry", []): patient = Patient.parse_obj(entry["resource"]) name = patient.name[0] print(f"{name.family}, {name.given[0]}")
pythonpatient = Patient( name=[{"family": "Doe", "given": ["John"], "use": "official"}], birthDate="1980-05-15", gender="male", ) resp = requests.post( f"{base_url}/Patient", json=patient.dict(), headers={"Content-Type": "application/fhir+json"}, ) print(f"Created: {resp.json()['id']}")
$everything for comprehensive patient data| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 12,946 | 8,003 | -38% | 1 | 1 | 0% | 2,720 | 2,219 | -18% | 0 | 0 | — |
case-02 | fail→pass | 13,279 | 8,857 | -33% | 1 | 1 | 0% | 2,526 | 2,070 | -18% | 0 | 0 | — |
case-03 | pass→pass | 3,770 | 3,046 | -19% | 1 | 1 | 0% | 704 | 885 | +26% | 0 | 0 | — |
case-04 | pass→fail | 12,174 | 9,382 | -23% | 1 | 1 | 0% | 2,404 | 2,198 | -9% | 0 | 0 | — |
case-10 | fail→pass | 7,498 | 4,506 | -40% | 1 | 1 | 0% | 1,493 | 1,246 | -17% | 0 | 0 | — |
case-05 | pass→pass | 7,633 | 3,692 | -52% | 1 | 1 | 0% | 1,489 | 1,103 | -26% | 0 | 0 | — |
case-06 | pass→pass | 4,583 | 1,872 | -59% | 1 | 1 | 0% | 745 | 610 | -18% | 0 | 0 | — |
case-07 | pass→pass | 9,255 | 3,314 | -64% | 1 | 1 | 0% | 1,776 | 927 | -48% | 0 | 0 | — |
case-08 | pass→pass | 5,621 | 1,905 | -66% | 1 | 1 | 0% | 941 | 709 | -25% | 0 | 0 | — |
case-09 | pass→pass | 7,330 | 5,576 | -24% | 1 | 1 | 0% | 1,520 | 1,405 | -8% | 0 | 0 | — |
case-11 | pass→pass | 2,600 | 2,412 | -7% | 1 | 1 | 0% | 393 | 721 | +83% | 0 | 0 | — |
case-12 | pass→pass | 6,544 | 4,929 | -25% | 1 | 1 | 0% | 1,119 | 1,244 | +11% | 0 | 0 | — |
case-13 | pass→pass | 4,618 | 3,585 | -22% | 1 | 1 | 0% | 727 | 970 | +33% | 0 | 0 | — |
case-14 | pass→pass | 7,004 | 4,955 | -29% | 1 | 1 | 0% | 1,206 | 1,181 | -2% | 0 | 0 | — |
case-15 | pass→pass | 2,884 | 3,050 | +6% | 1 | 1 | 0% | 409 | 914 | +123% | 0 | 0 | — |
case-16 | pass→pass | 7,499 | 4,712 | -37% | 1 | 1 | 0% | 1,497 | 1,214 | -19% | 0 | 0 | — |
case-17 | pass→pass | 12,794 | 8,191 | -36% | 1 | 1 | 0% | 2,203 | 1,814 | -18% | 0 | 0 | — |
case-18 | fail→pass | 11,298 | 4,913 | -57% | 1 | 1 | 0% | 1,826 | 1,355 | -26% | 0 | 0 | — |
case-19 | pass→pass | 17,733 | 12,307 | -31% | 1 | 1 | 0% | 3,557 | 2,763 | -22% | 0 | 0 | — |
case-20 | pass→pass | 9,439 | 6,326 | -33% | 1 | 1 | 0% | 1,712 | 1,608 | -6% | 0 | 0 | — |
case-21 | pass→pass | 19,737 | 10,402 | -47% | 1 | 1 | 0% | 4,117 | 2,427 | -41% | 0 | 0 | — |
case-22 | pass→pass | 12,828 | 7,982 | -38% | 1 | 1 | 0% | 2,608 | 1,904 | -27% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.