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case-summary
Prepare a complete clinical case summary for a patient from FHIR endpoints. Use when asked to summarize a patient, compile a case, or prepare for tumor board.
Prepare a complete clinical case summary for a patient from FHIR endpoints. Use when asked to summarize a patient, compile a case, or prepare for tumor board.
Diagnose common DGX Station GB300 issues — CUDA crashes, wrong-GPU targeting, vLLM/SGLang container bugs, MIG state problems, NVLink/Fabric Manager errors, X/Vulkan failures, HuggingFace auth, and port conflicts. Use when the user reports a GPU error, inference server crash, MIG problem, or any unexplained DGX Station failure.
Configure NVIDIA MIG (Multi-Instance GPU) partitions on the DGX Station GB300, including enabling MIG mode, choosing a profile layout, creating instances, and retrieving MIG UUIDs. Use when the user asks to partition the GB300, set up MIG, run multiple models in isolation on one GPU, or reconfigure existing MIG instances.
Deploy an SGLang inference server on an NVIDIA DGX Station GB300 with the cu130 container, RadixAttention prefix caching, and structured JSON output support. Use when the user asks to serve a model with SGLang, start an SGLang endpoint, or needs structured-output inference on DGX Station.
Deploy a vLLM inference server on an NVIDIA DGX Station GB300 with validated container, GPU targeting, and tuning parameters. Use when the user asks to serve a model with vLLM, start a vLLM endpoint, or set up OpenAI-compatible inference on DGX Station.
Teaches the analyst agent how to write correct, robust Python analysis code for FHIR clinical data using pandas, matplotlib, and scipy.
How to delegate clinical tasks to specialist agents. Always use sub-agent runtime with explicit agentId — never ACP. Never call FHIR via web_fetch.
| name | case-summary |
| description | Prepare a complete clinical case summary for a patient from FHIR endpoints. Use when asked to summarize a patient, compile a case, or prepare for tumor board. |
| metadata | {"openclaw":{"requires":{"bins":["python3"]}}} |
Prepare a complete case summary for $ARGUMENTS
Use your fhir-basics skill to query the FHIR endpoints. Use your clinical-knowledge skill to flag abnormal values and identify relevant clinical context.
Find the patient -- Query GET /Patient?name={name}&_count=5 or GET /Patient?_count=1 for "first patient". Extract id, full name, birthDate, gender.
Get active conditions -- Query GET /Condition?patient={id}&clinical-status=active. Extract each condition's display name, SNOMED/ICD code, onset date, and verification status. Also query for resolved conditions and list them separately (they provide clinical history context).
Get recent labs -- Query GET /Observation?patient={id}&category=laboratory&_sort=-date&_count=50. "Recent" means the most recent value for each distinct LOINC code within the past 12 months. For each lab, report: name, value, unit, date, and whether it's normal/abnormal per the clinical-knowledge skill. If the Observation includes a referenceRange, use that for flagging.
Get recent vitals -- Query GET /Observation?patient={id}&category=vital-signs&_sort=-date&_count=20. For blood pressure, handle the component Observation format (LOINC 85354-9 panel with systolic/diastolic in component[]). Report the most recent BP, heart rate, BMI, temperature.
Get current medications -- Query GET /MedicationRequest?patient={id}&status=active. For each medication, report: drug name, dosage text, and drug class (per clinical-knowledge skill). Organize by drug class when possible.
Get recent encounters (optional but adds context) -- Query GET /Encounter?patient={id}&_sort=-date&_count=5. Report type, date, and reason if available. This shows how recently the patient was seen.
Compile the case summary with these sections:
Disclaimer: "This summary is auto-generated from FHIR data for research and operational use. Verify all information before clinical decision-making."
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PATIENT CASE SUMMARY
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Generated: {date/time}
Source: {FHIR endpoint URL}
DEMOGRAPHICS
Name: {full name}
Age: {age} years (DOB: {birthDate})
Gender: {gender}
ACTIVE CONDITIONS ({count})
1. {display} (SNOMED {code}) -- onset {date}
2. ...
RESOLVED CONDITIONS ({count})
1. {display} -- resolved
RECENT LABS (most recent per test, past 12 months)
Metabolic:
HbA1c: {value}% ({date}) ⚠ ABOVE TARGET (>7.0%)
Fasting Glucose: {value} mg/dL ({date}) -- Normal
Renal:
Creatinine: {value} mg/dL ({date}) -- Normal
eGFR: {value} mL/min ({date}) -- Normal
Lipids:
LDL: {value} mg/dL ({date}) ⚠ ELEVATED (>100)
...
RECENT VITALS
Blood Pressure: {systolic}/{diastolic} mmHg ({date})
Heart Rate: {value} bpm ({date})
CURRENT MEDICATIONS ({count})
Diabetes:
- Metformin 1000mg twice daily
Antihypertensive:
- Lisinopril 20mg daily
Statin:
- Atorvastatin 40mg daily
CLINICAL FLAGS
⚠ HbA1c 9.2% indicates poor glycemic control (CMS122 gap)
⚠ LDL 165 mg/dL above target for diabetic patient
✓ On statin therapy (appropriate for diabetes + elevated LDL)
✓ On ACE inhibitor (appropriate for diabetes + hypertension)
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Disclaimer: This summary is auto-generated from FHIR data
for research and operational use. Verify all information
before clinical decision-making.
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