[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-clinical-knowledge":3,"mdc--hnzzmv-key":34,"related-repo-nvidia-clinical-knowledge":1950,"related-org-nvidia-clinical-knowledge":2047},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":32,"mdContent":33},"clinical-knowledge","provide clinical reference and regulatory context","Teaches agents clinical reference ranges, condition codes, quality measure definitions, drug classifications, and regulatory context so they can flag abnormal values and identify care gaps.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Healthcare","healthcare","tag",{"name":17,"slug":18,"type":15},"Clinical Trials","clinical-trials",{"name":20,"slug":21,"type":15},"Regulatory Compliance","regulatory-compliance",{"name":9,"slug":8,"type":15},1144,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fdgx-spark-playbooks","2026-07-14T05:35:56.550833",null,249,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Collection of step-by-step playbooks for setting up AI\u002FML workloads on NVIDIA DGX Spark devices with Blackwell architecture.","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fdgx-spark-playbooks\u002Ftree\u002FHEAD\u002Fnvidia\u002Fstation-healthcare-agent\u002Fassets\u002Fskills\u002Fclinical-knowledge","---\nname: clinical-knowledge\ndescription: Teaches agents clinical reference ranges, condition codes, quality measure definitions, drug classifications, and regulatory context so they can flag abnormal values and identify care gaps.\nmetadata:\n  openclaw:\n    requires:\n      bins: [\"python3\"]\n---\n\n# Clinical Reference Knowledge\n\n## Regulatory Context\n\nThe **21st Century Cures Act** (2016) and ONC's Interoperability Final Rule (2020) require US healthcare organizations to expose patient data through standardized FHIR APIs. As of 2024, ~70% of US hospitals support FHIR R4 (source: ONC). CMS ties quality measure reporting to reimbursement through programs like MIPS (Merit-based Incentive Payment System) and the Hospital Value-Based Purchasing Program. Failure to report -- or poor performance -- results in payment adjustments of up to 9%.\n\n**HIPAA** (Health Insurance Portability and Accountability Act) prohibits transmitting Protected Health Information (PHI) to external services without a BAA (Business Associate Agreement). This is the primary reason clinical AI must run locally: cloud LLM APIs are not BAA-covered by default, and even those that offer BAAs (e.g., Azure OpenAI) face institutional resistance from hospital compliance teams.\n\n## Common Lab Reference Ranges\n\nThese are general adult reference ranges. Values vary by lab, assay, and patient characteristics. Always defer to the performing laboratory's reference range when available via FHIR `referenceRange`.\n\n| Lab | Normal Range | Concerning | Unit | Notes |\n|-----|-------------|------------|------|-------|\n| HbA1c | \u003C 5.7% (non-diabetic), \u003C 7.0% (diabetic target) | > 9.0% = poor control | % | ADA 2024 guidelines; target may be relaxed to \u003C 8.0% for elderly\u002Ffrail |\n| Fasting Glucose | 70-100 | 100-125 = prediabetes, >= 126 = diabetic | mg\u002FdL | Must be fasting; random glucose >= 200 also diagnostic |\n| Creatinine | 0.7-1.3 (male), 0.6-1.1 (female) | > 1.5 = impaired renal | mg\u002FdL | Affected by muscle mass; less reliable in elderly |\n| eGFR | > 90 (normal), 60-89 (mild decrease) | 30-59 = moderate CKD, 15-29 = severe, \u003C 15 = kidney failure | mL\u002Fmin\u002F1.73m2 | CKD-EPI 2021 equation (race-neutral) |\n| BUN | 7-20 | > 20 with rising creatinine = renal concern | mg\u002FdL | Elevated by dehydration, high protein diet |\n| Total Cholesterol | \u003C 200 | 200-239 = borderline, >= 240 = high | mg\u002FdL | |\n| LDL | \u003C 100 (general), \u003C 70 (high-risk ASCVD) | > 160 = high | mg\u002FdL | ACC\u002FAHA 2018 |\n| HDL | > 40 (male), > 50 (female) | \u003C 40 = cardiovascular risk factor | mg\u002FdL | |\n| Triglycerides | \u003C 150 | 150-499 = elevated, >= 500 = severe (pancreatitis risk) | mg\u002FdL | |\n| Systolic BP | \u003C 120 (normal), 120-129 (elevated) | 130-139 = Stage 1 HTN, >= 140 = Stage 2 HTN | mmHg | ACC\u002FAHA 2017; CMS165 uses 140\u002F90 threshold |\n| Diastolic BP | \u003C 80 | 80-89 = Stage 1 HTN, >= 90 = Stage 2 HTN | mmHg | |\n| BNP | \u003C 100 | 100-400 = possible HF, > 400 = likely HF | pg\u002FmL | Age-adjusted: higher cutoffs in elderly; obesity lowers BNP |\n| NT-proBNP | \u003C 300 (rule-out) | Age-stratified: >450 (\u003C50y), >900 (50-75y), >1800 (>75y) | pg\u002FmL | More stable than BNP; renal clearance affects levels |\n| Potassium | 3.5-5.0 | \u003C 3.5 = hypokalemia, > 5.5 = hyperkalemia (cardiac risk) | mEq\u002FL | Critical for patients on ACEi\u002FARB\u002Fspironolactone |\n| Sodium | 136-145 | \u003C 130 = moderate hyponatremia | mEq\u002FL | Common in HF patients |\n| ALT | 7-56 | > 3x ULN = significant hepatotoxicity | U\u002FL | Monitor with statin therapy |\n| Hemoglobin | 13.5-17.5 (male), 12.0-16.0 (female) | \u003C 12 (male) or \u003C 11 (female) = anemia | g\u002FdL | Common in CKD (erythropoietin deficiency) |\n\nWhen reporting lab values:\n- Always flag values outside the normal range with the severity (mild \u002F moderate \u002F severe)\n- Note the date of the observation -- a result from 2 years ago has different clinical significance than one from yesterday\n- If the FHIR Observation includes a `referenceRange`, use that instead of the table above\n\n## Condition Codes\n\n### SNOMED CT Codes (Primary)\n\n| Code | Condition | ICD-10 Equivalent | Prevalence (US adults) |\n|------|-----------|-------------------|----------------------|\n| 44054006 | Type 2 Diabetes Mellitus | E11.x | ~11% (37M) |\n| 46635009 | Type 1 Diabetes Mellitus | E10.x | ~0.5% (1.6M) |\n| 38341003 | Essential Hypertension | I10 | ~47% (116M) |\n| 84114007 | Heart Failure | I50.x | ~2.4% (6.7M) |\n| 40055000 | Chronic Kidney Disease | N18.x | ~15% (37M) |\n| 53741008 | Coronary Artery Disease | I25.x | ~7% (20M) |\n| 13645005 | COPD | J44.x | ~6% (16M) |\n| 195967001 | Asthma | J45.x | ~8% (25M) |\n| 49436004 | Atrial Fibrillation | I48.x | ~2% (6M) |\n| 73211009 | Diabetes (unspecified) | E11.9 | Used in older records |\n\n### ICD-10-CM to SNOMED Crosswalk\n\nWhen FHIR Condition resources use ICD-10 coding (system `http:\u002F\u002Fhl7.org\u002Ffhir\u002Fsid\u002Ficd-10-cm`), map as follows:\n- E11.* → Type 2 Diabetes (SNOMED 44054006)\n- E10.* → Type 1 Diabetes (SNOMED 46635009)\n- I10 → Essential Hypertension (SNOMED 38341003)\n- I50.* → Heart Failure (SNOMED 84114007)\n- N18.* → Chronic Kidney Disease (SNOMED 40055000)\n\nNote: A Condition resource may have both SNOMED and ICD-10 codes in the `coding` array. Always check all entries, not just `coding[0]`.\n\n## CMS Quality Measures\n\n### CMS122v12 -- Diabetes: Hemoglobin A1c (HbA1c) Poor Control (> 9%)\n\n| Component | Definition |\n|-----------|-----------|\n| **Denominator** | Patients 18-75 with diabetes (Type 1 or Type 2) and at least 2 encounters during the measurement period |\n| **Numerator** | Patients with most recent HbA1c > 9.0%, OR no HbA1c recorded during the measurement period |\n| **Exclusions** | Hospice care, palliative care, advanced illness with frailty (2+ encounters for advanced illness AND frailty diagnosis), dementia medications (donepezil, rivastigmine, memantine, galantamine) |\n| **Performance rate** | Lower is better (inverse measure) |\n| **Payment impact** | Part of MIPS quality reporting; affects Medicare reimbursement |\n\n### CMS165v12 -- Controlling High Blood Pressure\n\n| Component | Definition |\n|-----------|-----------|\n| **Denominator** | Patients 18-85 with essential hypertension diagnosed before or during the measurement period |\n| **Numerator** | Patients with most recent BP \u003C 140\u002F90 mmHg |\n| **Exclusions** | Hospice, palliative care, ESRD, kidney transplant, advanced illness with frailty, pregnancy |\n| **Performance rate** | Higher is better |\n| **Note** | BP must be measured during an outpatient encounter; home BP readings are not counted in the standard measure |\n\n### CMS135v12 -- Heart Failure: ACEi\u002FARB\u002FARNI Therapy for LVEF \u003C 40%\n\n| Component | Definition |\n|-----------|-----------|\n| **Denominator** | Patients 18+ with heart failure AND documented LVEF \u003C 40% (HFrEF) |\n| **Numerator** | Patients prescribed ACE inhibitor, ARB, or ARNI (sacubitril\u002Fvalsartan) |\n| **Exclusions** | Hospice, allergy\u002Fintolerance to all three classes, bilateral renal artery stenosis, pregnancy, hyperkalemia > 5.5 |\n| **Note** | LVEF data often in DiagnosticReport or CarePlan, not always queryable via Condition alone |\n\n### CMS134v12 -- Diabetes: Medical Attention for Nephropathy\n\n| Component | Definition |\n|-----------|-----------|\n| **Denominator** | Patients 18-75 with diabetes |\n| **Numerator** | Patients with nephropathy screening (urine albumin test) OR evidence of nephropathy treatment (ACEi\u002FARB) OR nephropathy diagnosis |\n| **Exclusions** | Hospice, palliative care, advanced illness with frailty |\n\n## Drug Classifications\n\nWhen checking medication coverage, recognize these drug class groupings. Matching should be **case-insensitive partial string matching** on the medication name from FHIR `medicationCodeableConcept.text` or `.coding[].display`.\n\n### Diabetes Medications\n\n| Class | Drugs | Notes |\n|-------|-------|-------|\n| Biguanide | metformin | First-line therapy |\n| Sulfonylureas | glipizide, glyburide, glimepiride | Hypoglycemia risk |\n| Insulin | insulin lispro, insulin glargine, insulin aspart, insulin detemir, insulin degludec, NPH insulin | Match any string containing \"insulin\" |\n| GLP-1 Receptor Agonists | liraglutide (Victoza), semaglutide (Ozempic\u002FWegovy\u002FRybelsus), dulaglutide (Trulicity), exenatide (Byetta\u002FBydureon), tirzepatide (Mounjaro) | Weight loss benefit; cardiovascular benefit |\n| SGLT2 Inhibitors | empagliflozin (Jardiance), dapagliflozin (Farxiga), canagliflozin (Invokana), ertugliflozin (Steglatro) | Cardiovascular + renal benefit; monitor for DKA |\n| DPP-4 Inhibitors | sitagliptin (Januvia), saxagliptin, linagliptin, alogliptin | Weight-neutral |\n| Thiazolidinediones | pioglitazone, rosiglitazone | HF risk; edema |\n\n### Antihypertensives\n\n| Class | Drugs | Notes |\n|-------|-------|-------|\n| ACE Inhibitors | lisinopril, enalapril, ramipril, benazepril, fosinopril, quinapril | Cough side effect; monitor K+ and creatinine |\n| ARBs | losartan, valsartan, irbesartan, olmesartan, telmisartan, candesartan, azilsartan | Alternative if ACEi cough |\n| ARNIs | sacubitril\u002Fvalsartan (Entresto) | HFrEF guideline-directed; do NOT combine with ACEi |\n| CCBs | amlodipine, nifedipine, diltiazem, verapamil | Diltiazem\u002Fverapamil contraindicated in HFrEF |\n| Beta-Blockers | metoprolol (tartrate or succinate), atenolol, carvedilol, bisoprolol, propranolol, nebivolol | Only carvedilol, metoprolol succinate, bisoprolol for HF |\n| Thiazide Diuretics | hydrochlorothiazide (HCTZ), chlorthalidone, indapamide | First-line for HTN |\n| Loop Diuretics | furosemide, bumetanide, torsemide | Volume management in HF, not primary HTN therapy |\n| Aldosterone Antagonists | spironolactone, eplerenone | HF benefit; monitor K+ |\n\n### Statins (HMG-CoA Reductase Inhibitors)\n\n| Intensity | Drugs |\n|-----------|-------|\n| High | atorvastatin 40-80mg, rosuvastatin 20-40mg |\n| Moderate | atorvastatin 10-20mg, rosuvastatin 5-10mg, simvastatin 20-40mg, pravastatin 40-80mg |\n| Low | simvastatin 10mg, pravastatin 10-20mg, lovastatin 20mg |\n\n### Heart Failure Medications (Guideline-Directed Medical Therapy)\n\nThe four pillars of HFrEF therapy (ACC\u002FAHA 2022):\n1. **ACEi\u002FARB\u002FARNI** -- sacubitril\u002Fvalsartan preferred over ACEi\u002FARB\n2. **Beta-Blocker** -- carvedilol, metoprolol succinate, or bisoprolol only\n3. **Aldosterone Antagonist** -- spironolactone or eplerenone (if eGFR > 30, K+ \u003C 5.0)\n4. **SGLT2 Inhibitor** -- dapagliflozin or empagliflozin (regardless of diabetes status)\n\n### Matching Strategy\n\n```\nmedication_name = fhir_med_text.lower()\n\nis_on_insulin = \"insulin\" in medication_name\nis_on_glp1 = any(drug in medication_name for drug in\n    [\"liraglutide\", \"semaglutide\", \"dulaglutide\", \"exenatide\", \"tirzepatide\",\n     \"victoza\", \"ozempic\", \"trulicity\", \"byetta\", \"mounjaro\", \"rybelsus\"])\nis_on_sglt2 = any(drug in medication_name for drug in\n    [\"empagliflozin\", \"dapagliflozin\", \"canagliflozin\", \"ertugliflozin\",\n     \"jardiance\", \"farxiga\", \"invokana\", \"steglatro\"])\nis_on_acei = any(drug in medication_name for drug in\n    [\"lisinopril\", \"enalapril\", \"ramipril\", \"benazepril\", \"fosinopril\", \"quinapril\"])\nis_on_arb = any(drug in medication_name for drug in\n    [\"losartan\", \"valsartan\", \"irbesartan\", \"olmesartan\", \"telmisartan\",\n     \"candesartan\", \"azilsartan\"])\nis_on_betablocker = any(drug in medication_name for drug in\n    [\"metoprolol\", \"atenolol\", \"carvedilol\", \"bisoprolol\", \"propranolol\", \"nebivolol\"])\nis_on_statin = any(drug in medication_name for drug in\n    [\"atorvastatin\", \"rosuvastatin\", \"simvastatin\", \"pravastatin\", \"lovastatin\"])\n```\n\n## Clinical Comorbidity Patterns\n\nCommon co-occurring conditions to watch for during analysis:\n- **Cardiorenal-metabolic overlap**: Diabetes + Hypertension + CKD occur together in ~30% of diabetic patients\n- **Heart failure + CKD**: eGFR \u003C 30 limits medication options (spironolactone, SGLT2i dose adjustment)\n- **Diabetes + CAD**: Statin therapy should be high-intensity; GLP-1\u002FSGLT2i have cardiovascular benefit\n- **Atrial fibrillation + Heart failure**: Common pairing; rate control with beta-blocker preferred\n\n## Guardrails\n\n- These reference ranges are general guidelines based on published clinical guidelines (ADA, ACC\u002FAHA, KDIGO), not diagnostic criteria\n- Never state that a patient \"has\" a condition based on a lab value alone\n- Always include the disclaimer: \"This is for informational and research purposes, not clinical decision-making\"\n- When flagging care gaps, use language like \"may warrant review\" not \"requires treatment\"\n- Acknowledge that CMS measure logic here is simplified -- production implementations use the full eCQM (electronic Clinical Quality Measure) specifications from CMS\n- Lab reference ranges should defer to the performing laboratory's range when available\n",{"data":35,"body":41},{"name":4,"description":6,"metadata":36},{"openclaw":37},{"requires":38},{"bins":39},[40],"python3",{"type":42,"children":43},"root",[44,53,60,74,84,90,104,606,611,638,644,651,913,919,932,960,980,986,992,1094,1100,1196,1202,1282,1288,1353,1359,1386,1392,1544,1550,1718,1724,1784,1790,1795,1839,1845,1857,1863,1868,1911,1917],{"type":45,"tag":46,"props":47,"children":49},"element","h1",{"id":48},"clinical-reference-knowledge",[50],{"type":51,"value":52},"text","Clinical Reference Knowledge",{"type":45,"tag":54,"props":55,"children":57},"h2",{"id":56},"regulatory-context",[58],{"type":51,"value":59},"Regulatory Context",{"type":45,"tag":61,"props":62,"children":63},"p",{},[64,66,72],{"type":51,"value":65},"The ",{"type":45,"tag":67,"props":68,"children":69},"strong",{},[70],{"type":51,"value":71},"21st Century Cures Act",{"type":51,"value":73}," (2016) and ONC's Interoperability Final Rule (2020) require US healthcare organizations to expose patient data through standardized FHIR APIs. 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5.0)",{"type":45,"tag":616,"props":1830,"children":1831},{},[1832,1837],{"type":45,"tag":67,"props":1833,"children":1834},{},[1835],{"type":51,"value":1836},"SGLT2 Inhibitor",{"type":51,"value":1838}," -- dapagliflozin or empagliflozin (regardless of diabetes status)",{"type":45,"tag":645,"props":1840,"children":1842},{"id":1841},"matching-strategy",[1843],{"type":51,"value":1844},"Matching Strategy",{"type":45,"tag":1846,"props":1847,"children":1851},"pre",{"className":1848,"code":1850,"language":51},[1849],"language-text","medication_name = fhir_med_text.lower()\n\nis_on_insulin = \"insulin\" in medication_name\nis_on_glp1 = any(drug in medication_name for drug in\n    [\"liraglutide\", \"semaglutide\", \"dulaglutide\", \"exenatide\", \"tirzepatide\",\n     \"victoza\", \"ozempic\", \"trulicity\", \"byetta\", \"mounjaro\", \"rybelsus\"])\nis_on_sglt2 = any(drug in medication_name for drug in\n    [\"empagliflozin\", \"dapagliflozin\", \"canagliflozin\", \"ertugliflozin\",\n     \"jardiance\", \"farxiga\", \"invokana\", \"steglatro\"])\nis_on_acei = any(drug in medication_name for drug in\n    [\"lisinopril\", \"enalapril\", \"ramipril\", \"benazepril\", \"fosinopril\", \"quinapril\"])\nis_on_arb = any(drug in medication_name for drug in\n    [\"losartan\", \"valsartan\", \"irbesartan\", \"olmesartan\", \"telmisartan\",\n     \"candesartan\", \"azilsartan\"])\nis_on_betablocker = any(drug in medication_name for drug in\n    [\"metoprolol\", \"atenolol\", \"carvedilol\", \"bisoprolol\", \"propranolol\", \"nebivolol\"])\nis_on_statin = any(drug in medication_name for drug in\n    [\"atorvastatin\", \"rosuvastatin\", \"simvastatin\", \"pravastatin\", \"lovastatin\"])\n",[1852],{"type":45,"tag":96,"props":1853,"children":1855},{"__ignoreMap":1854},"",[1856],{"type":51,"value":1850},{"type":45,"tag":54,"props":1858,"children":1860},{"id":1859},"clinical-comorbidity-patterns",[1861],{"type":51,"value":1862},"Clinical Comorbidity Patterns",{"type":45,"tag":61,"props":1864,"children":1865},{},[1866],{"type":51,"value":1867},"Common co-occurring conditions to watch for during analysis:",{"type":45,"tag":612,"props":1869,"children":1870},{},[1871,1881,1891,1901],{"type":45,"tag":616,"props":1872,"children":1873},{},[1874,1879],{"type":45,"tag":67,"props":1875,"children":1876},{},[1877],{"type":51,"value":1878},"Cardiorenal-metabolic overlap",{"type":51,"value":1880},": Diabetes + Hypertension + CKD occur together in ~30% of diabetic patients",{"type":45,"tag":616,"props":1882,"children":1883},{},[1884,1889],{"type":45,"tag":67,"props":1885,"children":1886},{},[1887],{"type":51,"value":1888},"Heart failure + CKD",{"type":51,"value":1890},": eGFR \u003C 30 limits medication options (spironolactone, SGLT2i dose adjustment)",{"type":45,"tag":616,"props":1892,"children":1893},{},[1894,1899],{"type":45,"tag":67,"props":1895,"children":1896},{},[1897],{"type":51,"value":1898},"Diabetes + CAD",{"type":51,"value":1900},": Statin therapy should be high-intensity; GLP-1\u002FSGLT2i have cardiovascular benefit",{"type":45,"tag":616,"props":1902,"children":1903},{},[1904,1909],{"type":45,"tag":67,"props":1905,"children":1906},{},[1907],{"type":51,"value":1908},"Atrial fibrillation + Heart failure",{"type":51,"value":1910},": Common pairing; rate control with beta-blocker preferred",{"type":45,"tag":54,"props":1912,"children":1914},{"id":1913},"guardrails",[1915],{"type":51,"value":1916},"Guardrails",{"type":45,"tag":612,"props":1918,"children":1919},{},[1920,1925,1930,1935,1940,1945],{"type":45,"tag":616,"props":1921,"children":1922},{},[1923],{"type":51,"value":1924},"These reference ranges are general guidelines based on published clinical guidelines (ADA, ACC\u002FAHA, KDIGO), not diagnostic criteria",{"type":45,"tag":616,"props":1926,"children":1927},{},[1928],{"type":51,"value":1929},"Never state that a patient \"has\" a condition based on a lab value alone",{"type":45,"tag":616,"props":1931,"children":1932},{},[1933],{"type":51,"value":1934},"Always include the disclaimer: \"This is for informational and research purposes, not clinical decision-making\"",{"type":45,"tag":616,"props":1936,"children":1937},{},[1938],{"type":51,"value":1939},"When flagging care gaps, use language like \"may warrant review\" not \"requires treatment\"",{"type":45,"tag":616,"props":1941,"children":1942},{},[1943],{"type":51,"value":1944},"Acknowledge that CMS measure logic here is simplified -- production implementations use the full eCQM (electronic Clinical Quality Measure) specifications from CMS",{"type":45,"tag":616,"props":1946,"children":1947},{},[1948],{"type":51,"value":1949},"Lab reference ranges should defer to the performing laboratory's range when available",{"items":1951,"total":2046},[1952,1970,1983,1998,2005,2016,2033],{"slug":1953,"name":1953,"fn":1954,"description":1955,"org":1956,"tags":1957,"stars":23,"repoUrl":24,"updatedAt":1969},"analysis-methods","write Python analysis code for FHIR data","Teaches the analyst agent how to write correct, robust Python analysis code for FHIR clinical data using pandas, matplotlib, and scipy.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1958,1961,1964,1965,1966],{"name":1959,"slug":1960,"type":15},"Data Analysis","data-analysis",{"name":1962,"slug":1963,"type":15},"FHIR","fhir",{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":1967,"slug":1968,"type":15},"Python","python","2026-07-14T05:35:59.037962",{"slug":1971,"name":1971,"fn":1972,"description":1973,"org":1974,"tags":1975,"stars":23,"repoUrl":24,"updatedAt":1982},"case-summary","summarize clinical patient cases from FHIR","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1976,1977,1978,1979],{"name":1962,"slug":1963,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":1980,"slug":1981,"type":15},"Summarization","summarization","2026-07-14T05:35:52.790528",{"slug":1984,"name":1984,"fn":1985,"description":1986,"org":1987,"tags":1988,"stars":23,"repoUrl":24,"updatedAt":1997},"clinical-delegation","delegate clinical tasks to specialist agents","How to delegate clinical tasks to specialist agents. Always use sub-agent runtime with explicit agentId — never ACP. Never call FHIR via web_fetch.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1989,1992,1993,1996],{"name":1990,"slug":1991,"type":15},"Agents","agents",{"name":13,"slug":14,"type":15},{"name":1994,"slug":1995,"type":15},"Multi-Agent","multi-agent",{"name":9,"slug":8,"type":15},"2026-07-14T05:35:55.294972",{"slug":4,"name":4,"fn":5,"description":6,"org":1999,"tags":2000,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2001,2002,2003,2004],{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},{"name":20,"slug":21,"type":15},{"slug":2006,"name":2006,"fn":2007,"description":2008,"org":2009,"tags":2010,"stars":23,"repoUrl":24,"updatedAt":2015},"cohort-compare","analyze patient cohorts from FHIR endpoints","Analyze a cohort of patients from FHIR endpoints to find care gaps and patterns. Use when asked to compare patients, find quality gaps, or analyze a population.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2011,2012,2013,2014],{"name":1959,"slug":1960,"type":15},{"name":1962,"slug":1963,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:35:51.543095",{"slug":2017,"name":2017,"fn":2018,"description":2019,"org":2020,"tags":2021,"stars":23,"repoUrl":24,"updatedAt":2032},"dgx-diagnose","diagnose NVIDIA DGX Station hardware issues","Diagnose common DGX Station GB300 issues — CUDA crashes, wrong-GPU targeting, vLLM\u002FSGLang container bugs, MIG state problems, NVLink\u002FFabric Manager errors, X\u002FVulkan 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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2022,2025,2028,2029],{"name":2023,"slug":2024,"type":15},"AI Infrastructure","ai-infrastructure",{"name":2026,"slug":2027,"type":15},"Debugging","debugging",{"name":9,"slug":8,"type":15},{"name":2030,"slug":2031,"type":15},"Observability","observability","2026-07-14T05:31:04.085598",{"slug":2034,"name":2034,"fn":2035,"description":2036,"org":2037,"tags":2038,"stars":23,"repoUrl":24,"updatedAt":2045},"fhir-basics","query and parse FHIR R4 API resources","Teaches agents how FHIR R4 APIs work, what resources are available, how to query them with search parameters, and how to correctly parse all response formats including component Observations.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2039,2042,2043,2044],{"name":2040,"slug":2041,"type":15},"API Development","api-development",{"name":1962,"slug":1963,"type":15},{"name":13,"slug":14,"type":15},{"name":9,"slug":8,"type":15},"2026-07-14T05:35:57.797226",11,{"items":2048,"total":2203},[2049,2067,2083,2094,2106,2118,2131,2145,2158,2169,2183,2192],{"slug":2050,"name":2050,"fn":2051,"description":2052,"org":2053,"tags":2054,"stars":2064,"repoUrl":2065,"updatedAt":2066},"nemoclaw-user-guide","retrieve NemoClaw documentation and configuration","Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2055,2058,2061],{"name":2056,"slug":2057,"type":15},"Documentation","documentation",{"name":2059,"slug":2060,"type":15},"MCP","mcp",{"name":2062,"slug":2063,"type":15},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":2068,"name":2068,"fn":2069,"description":2070,"org":2071,"tags":2072,"stars":2080,"repoUrl":2081,"updatedAt":2082},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2073,2076,2079],{"name":2074,"slug":2075,"type":15},"Containers","containers",{"name":2077,"slug":2078,"type":15},"Deployment","deployment",{"name":1967,"slug":1968,"type":15},17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":2084,"name":2084,"fn":2085,"description":2086,"org":2087,"tags":2088,"stars":2080,"repoUrl":2081,"updatedAt":2093},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2089,2092],{"name":2090,"slug":2091,"type":15},"CI\u002FCD","ci-cd",{"name":2077,"slug":2078,"type":15},"2026-07-14T05:25:59.97109",{"slug":2095,"name":2095,"fn":2096,"description":2097,"org":2098,"tags":2099,"stars":2080,"repoUrl":2081,"updatedAt":2105},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2100,2101,2102],{"name":2090,"slug":2091,"type":15},{"name":2077,"slug":2078,"type":15},{"name":2103,"slug":2104,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":2107,"name":2107,"fn":2108,"description":2109,"org":2110,"tags":2111,"stars":2080,"repoUrl":2081,"updatedAt":2117},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2112,2113,2114],{"name":2026,"slug":2027,"type":15},{"name":2103,"slug":2104,"type":15},{"name":2115,"slug":2116,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":2119,"name":2119,"fn":2120,"description":2121,"org":2122,"tags":2123,"stars":2080,"repoUrl":2081,"updatedAt":2130},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2124,2127],{"name":2125,"slug":2126,"type":15},"Best Practices","best-practices",{"name":2128,"slug":2129,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":2132,"name":2132,"fn":2133,"description":2134,"org":2135,"tags":2136,"stars":2080,"repoUrl":2081,"updatedAt":2144},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2137,2140,2143],{"name":2138,"slug":2139,"type":15},"Machine Learning","machine-learning",{"name":2141,"slug":2142,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":2146,"name":2146,"fn":2147,"description":2148,"org":2149,"tags":2150,"stars":2080,"repoUrl":2081,"updatedAt":2157},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2151,2154],{"name":2152,"slug":2153,"type":15},"QA","qa",{"name":2155,"slug":2156,"type":15},"Testing","testing","2026-07-14T05:25:53.673039",{"slug":2159,"name":2159,"fn":2160,"description":2161,"org":2162,"tags":2163,"stars":2080,"repoUrl":2081,"updatedAt":2168},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2164,2165],{"name":2077,"slug":2078,"type":15},{"name":2166,"slug":2167,"type":15},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":2170,"name":2170,"fn":2171,"description":2172,"org":2173,"tags":2174,"stars":2080,"repoUrl":2081,"updatedAt":2182},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2175,2178,2179],{"name":2176,"slug":2177,"type":15},"Code Review","code-review",{"name":2103,"slug":2104,"type":15},{"name":2180,"slug":2181,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":2184,"name":2184,"fn":2185,"description":2186,"org":2187,"tags":2188,"stars":2080,"repoUrl":2081,"updatedAt":2191},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2189,2190],{"name":2152,"slug":2153,"type":15},{"name":2155,"slug":2156,"type":15},"2026-07-14T05:25:54.928983",{"slug":2193,"name":2193,"fn":2194,"description":2195,"org":2196,"tags":2197,"stars":2080,"repoUrl":2081,"updatedAt":2202},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2198,2201],{"name":2199,"slug":2200,"type":15},"Automation","automation",{"name":2090,"slug":2091,"type":15},"2026-07-30T05:29:03.275638",496]