[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-pa-clinical-policy":3,"mdc-wkqywk-key":53,"related-repo-aws-labs-pa-clinical-policy":2397,"related-org-aws-labs-pa-clinical-policy":2497},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":29,"repoUrl":30,"updatedAt":31,"license":32,"forks":33,"topics":34,"repo":48,"sourceUrl":51,"mdContent":52},"pa-clinical-policy","evaluate clinical prior authorization policies","Reasoning skill for prior authorization clinical policy evaluation. Use when the user asks about payer clinical criteria, step therapy requirements, medical necessity definitions, CMS LCD\u002FNCD coverage rules, appeals documentation strategy, formulary tier implications, or FHIR Da Vinci PAS implementation guidance. Triggers include \"prior auth policy\", \"step therapy\", \"medical necessity\", \"coverage determination\", \"LCD\", \"NCD\", \"formulary tier\", \"PA appeal\", \"peer-to-peer review\", \"Da Vinci PAS\", \"clinical criteria\", \"PA denial\", \"drug authorization\", \"utilization management\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"aws-labs","AWS Labs","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Faws-labs.png","awslabs",[13,17,20,23,26],{"name":14,"slug":15,"type":16},"Compliance","compliance","tag",{"name":18,"slug":19,"type":16},"Healthcare","healthcare",{"name":21,"slug":22,"type":16},"Medical Necessity","medical-necessity",{"name":24,"slug":25,"type":16},"Insurance","insurance",{"name":27,"slug":28,"type":16},"Policy","policy",4,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fhcls-agent-skills","2026-07-12T08:37:46.137142",null,0,[35,36,37,38,39,40,41,42,43,44,45,46,47],"agent-skills","agentcore","ai-agents","amazon-quick-desktop","claims-processing","drug-discovery","genomics","healthcare-ai","kiro","life-sciences","medical-imaging","risk-adjustment","strands-agents",{"repoUrl":30,"stars":29,"forks":33,"topics":49,"description":50},[35,36,37,38,39,40,41,42,43,44,45,46,47],"Agent skills for healthcare and life sciences: genomics, imaging, claims, drug discovery, and more. Works with Amazon Quick, Kiro, Amazon AgentCore, AWS Strands SDK, Claude Code, Codex, and any Agent Skills-compatible platform.","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fhcls-agent-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fpa-clinical-policy","---\nname: pa-clinical-policy\ndescription: >\n  Reasoning skill for prior authorization clinical policy evaluation. Use when the user asks about\n  payer clinical criteria, step therapy requirements, medical necessity definitions, CMS LCD\u002FNCD\n  coverage rules, appeals documentation strategy, formulary tier implications, or FHIR Da Vinci PAS\n  implementation guidance. Triggers include \"prior auth policy\", \"step therapy\", \"medical necessity\",\n  \"coverage determination\", \"LCD\", \"NCD\", \"formulary tier\", \"PA appeal\", \"peer-to-peer review\",\n  \"Da Vinci PAS\", \"clinical criteria\", \"PA denial\", \"drug authorization\", \"utilization management\".\nusage: Use when evaluating prior authorization clinical policies, coverage rules, or appeal strategies.\nversion: 1.0.0\ntags: [skill, category:reasoning, prior-authorization, clinical-policy, hcls]\n---\n\n# Prior Authorization Clinical Policy Reasoning\n\n## Overview\n\nGuide the agent through structured evaluation of prior authorization (PA) clinical policies,\ncoverage determinations, step therapy protocols, and appeals processes. This skill encodes\npayer policy logic so the agent can assess whether a requested service meets authorization\ncriteria, identify documentation gaps, and recommend appeal strategies.\n\n## Usage\n\n- Invoke when evaluating whether a service meets prior authorization clinical criteria\n- Use for step therapy evaluation, medical necessity assessment, or appeal strategy\n- Activate for CMS LCD\u002FNCD coverage rules or Da Vinci PAS implementation guidance\n\n## Core Concepts\n\n## Response Format\n\n- Lead with the direct recommendation or classification (≤3 sentences)\n- Structure as: recommendation → justification (citing specific criteria\u002Fthresholds) → caveats\n- Use tables for comparisons; bullet points for criteria lists\n- Omit background the user already knows — they asked the question\n- Target: 200-400 words unless the user requests exhaustive detail\nThe decision trees and frameworks in this skill are for internal reasoning only. Apply them to reach your conclusion, but do not reproduce them in your response. Present only the final recommendation with supporting evidence.\n\n\n## 1. Prior Authorization Decision Framework\n\nWhen a user asks about a PA decision, follow this sequence:\n\n1. **Identify the service type**: drug (pharmacy benefit), procedure, DME, or imaging\n2. **Determine the payer**: commercial, Medicare, Medicaid, or Medicare Advantage\n3. **Locate the applicable policy**: formulary, LCD\u002FNCD, or internal clinical criteria\n4. **Evaluate medical necessity**: match diagnosis + clinical evidence to criteria\n5. **Check step therapy**: confirm required prior treatments were attempted\n6. **Assess documentation completeness**: verify all required supporting information\n7. **Recommend action**: approve path, identify gaps, or outline appeal strategy\n\n## 2. Clinical Criteria Structures\n\n### 2.1 Medical Necessity Definition\n\nMedical necessity requires ALL of the following:\n\n1. **Clinically appropriate**: consistent with diagnosis, symptoms, and accepted standards of care\n2. **Not primarily for convenience**: of the patient, provider, or payer\n3. **Most cost-effective level**: among equally effective alternatives\n4. **Not experimental**: FDA-approved or supported by peer-reviewed evidence\n5. **Expected to improve outcome**: measurable clinical benefit anticipated\n\n### 2.3 Diagnosis-Specific Criteria Examples\n\n| Condition | Requested Service | Required Criteria |\n|-----------|-------------------|-------------------|\n| Rheumatoid Arthritis | Biologic (TNF inhibitor) | Failure of ≥1 conventional DMARD (methotrexate) for ≥3 months |\n| Multiple Sclerosis | Disease-modifying therapy | Confirmed MS diagnosis (McDonald criteria), relapse history |\n| Chronic Pain | Opioid >90 MME\u002Fday | Pain management referral, urine drug screen, treatment agreement |\n| Diabetes (Type 2) | GLP-1 receptor agonist | HbA1c ≥7% on metformin, or metformin contraindication documented |\n| Cancer | PET\u002FCT scan | Staging of newly diagnosed cancer or restaging after treatment |\n| Sleep Apnea | CPAP device | AHI ≥5 on polysomnography, clinical symptoms documented |\n\n## 3. Step Therapy Requirements\n\n### 3.1 Step Therapy Logic\n\nStep therapy mandates that lower-cost or first-line treatments are tried before authorizing\nhigher-cost alternatives. The general pattern:\n\n```\nStep 1: Generic \u002F first-line therapy\n  ↓ (documented failure, intolerance, or contraindication)\nStep 2: Preferred brand \u002F second-line therapy\n  ↓ (documented failure, intolerance, or contraindication)\nStep 3: Non-preferred \u002F specialty therapy\n```\n\n### 3.2 Step Therapy Evaluation Rules\n\n1. **Adequate trial duration**: each step must be tried for the clinically appropriate duration\n   - Most oral medications: 30–90 days\n   - Biologics: 12–16 weeks\n   - Behavioral health: 8–12 weeks\n2. **Documented failure**: objective evidence of inadequate response (lab values, symptom scores)\n3. **Intolerance**: documented adverse effects that preclude continued use\n4. **Contraindication**: clinical reason the step cannot be attempted (allergy, drug interaction, comorbidity)\n5. **Step skip exceptions**: life-threatening conditions, prior step completed at another plan\n\n### 3.3 Common Step Therapy Sequences\n\n| Drug Class | Step 1 | Step 2 | Step 3 |\n|------------|--------|--------|--------|\n| Statins | Generic atorvastatin\u002Frosuvastatin | Preferred brand statin | PCSK9 inhibitor |\n| Antidepressants | Generic SSRI (sertraline, fluoxetine) | Generic SNRI (venlafaxine) | Brand atypical (Trintellix) |\n| Biologics (RA) | Methotrexate + conventional DMARD | Preferred TNF inhibitor | Non-preferred biologic\u002FJAK inhibitor |\n| Diabetes | Metformin | Sulfonylurea or SGLT2 | GLP-1 RA or insulin |\n| Asthma | ICS (fluticasone) | ICS\u002FLABA combination | Biologic (omalizumab, dupilumab) |\n\n## 4. CMS Coverage Determinations\n\n### 4.1 NCD vs LCD\n\n| Attribute | NCD (National) | LCD (Local) |\n|-----------|----------------|-------------|\n| Issuer | CMS central | Medicare Administrative Contractor (MAC) |\n| Scope | All Medicare nationwide | MAC jurisdiction (A\u002FB or DME) |\n| Override | Cannot be overridden locally | Must comply with any applicable NCD |\n| Appeal path | ALJ → Medicare Appeals Council → Federal court | Redetermination → QIC → ALJ |\n| Update frequency | Infrequent (years) | More frequent (annual review) |\n\n### 4.2 LCD Evaluation Checklist\n\nWhen assessing whether a service is covered under an LCD:\n\n- [ ] Identify the MAC jurisdiction for the provider's location\n- [ ] Search the CMS Medicare Coverage Database for active LCDs\n- [ ] Check the LCD's ICD-10 code list — is the patient's diagnosis included?\n- [ ] Review the \"Indications and Limitations\" section for clinical criteria\n- [ ] Verify the CPT\u002FHCPCS code is listed as covered under the LCD\n- [ ] Check for any associated billing article with documentation requirements\n- [ ] Confirm no superseding NCD exists for the same service\n\n## 5. Formulary Tier Structure\n\n### 5.1 Formulary Exception Process\n\n1. **Standard exception**: prescriber submits clinical rationale for non-formulary drug\n2. **Expedited exception**: urgent clinical need, 24-hour turnaround required\n3. **Tier reduction**: request lower cost-sharing based on medical necessity\n4. **Required documentation**: letter of medical necessity, prior treatment history, lab results\n\n## 6. Appeals Process\n\n### 6.1 Appeal Levels (Commercial)\n\n| Level | Action | Timeline | Decision Maker |\n|-------|--------|----------|----------------|\n| 1 | Internal appeal | 30 days (standard), 72 hours (expedited) | Payer medical director |\n| 2 | External review | 45 days (standard), 72 hours (expedited) | Independent Review Organization (IRO) |\n| 3 | State regulatory | Varies by state | Department of Insurance |\n\n### 6.2 Appeal Levels (Medicare Part C\u002FD)\n\n| Level | Action | Timeline |\n|-------|--------|----------|\n| 1 | Plan redetermination | 7 days (expedited), 30 days (standard) |\n| 2 | Independent Review Entity (IRE) | 7 days (expedited), 30 days (standard) |\n| 3 | Administrative Law Judge (ALJ) | Amount in controversy ≥$180 (2024) |\n| 4 | Medicare Appeals Council | No minimum amount |\n| 5 | Federal District Court | Amount in controversy ≥$1,760 (2024) |\n\n### 6.3 Peer-to-Peer Review Best Practices\n\nWhen preparing for a peer-to-peer review with the payer medical director:\n\n1. **Know the specific denial reason**: request the denial letter and clinical policy cited\n2. **Prepare clinical evidence**: relevant labs, imaging, treatment history, specialist notes\n3. **Reference guidelines**: cite society guidelines (ACR, NCCN, AAN) supporting the request\n4. **Document prior treatments**: list all failed\u002Ftried therapies with dates and outcomes\n5. **Articulate medical necessity**: explain why this specific service is required for this patient\n6. **Note urgency**: if delay poses clinical risk, document the time-sensitive nature\n\n### 6.4 Documentation Checklist for Appeals\n\n- [ ] Copy of the denial letter with specific reason codes\n- [ ] Letter of medical necessity from treating physician\n- [ ] Relevant clinical notes (last 6–12 months)\n- [ ] Lab results supporting the diagnosis and treatment need\n- [ ] Prior treatment history with dates, doses, and outcomes\n- [ ] Society guideline excerpts supporting the requested service\n- [ ] Peer-reviewed literature (if off-label or emerging therapy)\n- [ ] Patient statement (if relevant to functional impact)\n\n## 7. FHIR Da Vinci PAS Implementation\n\n### 7.1 Overview\n\nThe Da Vinci Prior Authorization Support (PAS) Implementation Guide defines a FHIR-based\nworkflow for submitting and tracking prior authorization requests. CMS mandates payer support\nby 2026.\n\n### 7.2 PAS Workflow Sequence\n\n1. Provider EHR constructs a PAS `Bundle` with `Claim` + supporting resources\n2. EHR submits `$submit` operation to payer's PAS endpoint\n3. Payer returns `ClaimResponse` with disposition: `approved`, `denied`, or `pended`\n4. If pended, payer may request additional info via `CommunicationRequest`\n5. Provider submits updated `Bundle` with requested documentation\n6. Payer issues final `ClaimResponse`\n7. Provider queries `$inquire` operation for status updates\n\n## When NOT to Use This Skill\n\n- Making coverage determinations for individual patients (requires licensed clinician)\n- When payer-specific contracts override published clinical policies\n- Adjudicating appeals that require medical record review\n\n## When to Escalate to a Human Expert\n\n- Peer-to-peer review preparation (needs treating physician)\n- When denial involves experimental\u002Finvestigational determination\n- When state Medicaid rules conflict with commercial payer policies\n\n## 8. Common Mistakes\n\n- **Wrong:** Assuming all payers use the same clinical criteria for a given service\n  **Right:** Always check the specific payer's published clinical policy before submitting a PA request\n  **Why:** Each payer maintains independent policies; criteria that work for one payer may not apply to another\n\n- **Wrong:** Skipping step therapy documentation when the patient tried a drug at a prior plan\n  **Right:** Document all prior treatments with dates, doses, duration, and outcomes — even from previous plans\n  **Why:** Without documented evidence of prior steps, the payer will deny regardless of actual treatment history\n\n- **Wrong:** Submitting appeals without addressing the specific denial reason code or cited criteria\n  **Right:** Reference the exact denial reason and provide evidence directly addressing each unmet criterion\n  **Why:** Generic appeals that don't target the specific denial rationale are almost always upheld\n\n- **Wrong:** Applying an LCD from one MAC jurisdiction to a provider in a different MAC's territory\n  **Right:** Identify the correct MAC jurisdiction for the provider's location and use that MAC's LCD\n  **Why:** LCDs are jurisdiction-specific; coverage rules from one MAC have no authority in another\n\n- **Wrong:** Treating LCDs and NCDs as equivalent or interchangeable\n  **Right:** Always check for an applicable NCD first — NCDs take precedence and cannot be overridden by LCDs\n  **Why:** An LCD cannot contradict or override a National Coverage Determination\n\n- **Wrong:** Missing formulary exception deadlines, especially for expedited requests\n  **Right:** Track and meet all turnaround requirements — 24 hours for expedited exceptions\n  **Why:** Missed deadlines result in automatic denials and delayed patient access to needed medications\n\n- **Wrong:** Conducting peer-to-peer reviews without citing published clinical guidelines\n  **Right:** Reference specific society guidelines (ACR, NCCN, AAN) that support the requested service\n  **Why:** Peer-to-peer reviews are significantly more effective when backed by authoritative guideline citations\n\n- **Wrong:** Attaching clinical documentation as unstructured PDFs in Da Vinci PAS bundles\n  **Right:** Structure clinical documentation in the `supportingInfo` field using proper FHIR resource references\n  **Why:** Unstructured attachments cannot be processed by automated adjudication systems, causing delays\n\n- **Wrong:** Applying commercial appeal timelines and processes to Medicare PA requests\n  **Right:** Use Medicare-specific appeal levels and timelines (redetermination → IRE → ALJ → MAC → Federal court)\n  **Why:** Medicare has distinct appeal levels, timelines, and amount-in-controversy thresholds\n\n- **Wrong:** Failing to track PA expiration dates after authorization is granted\n  **Right:** Monitor authorization validity periods and reauthorize before expiration if services are ongoing\n  **Why:** Services rendered after PA expiration require new authorization — retroactive approval is rarely granted\n\n## 9. Decision Tree: PA Request Evaluation\n\n```\nIs the service on the payer's PA-required list?\n├── NO → No PA needed; proceed with service\n└── YES\n    ├── Is there an applicable NCD?\n    │   ├── YES → Does the request meet NCD criteria?\n    │   │   ├── YES → Approve (document NCD compliance)\n    │   │   └── NO → Deny (cite NCD; appeal to ALJ if Medicare)\n    │   └── NO → Check for LCD or plan-specific policy\n    │       ├── LCD exists → Evaluate LCD criteria\n    │       └── Plan policy exists → Evaluate plan criteria\n    │           ├── Medical necessity met?\n    │           │   ├── YES → Check step therapy\n    │           │   │   ├── Step therapy satisfied → Approve\n    │           │   │   └── Step therapy NOT satisfied\n    │           │   │       ├── Exception applies? → Approve with exception\n    │           │   │       └── No exception → Deny (cite step therapy)\n    │           │   └── NO → Deny (cite medical necessity)\n    │           └── Documentation incomplete?\n    │               └── Pend for additional information\n```\n\n## 10. Quick Reference: Denial Reason Categories\n\n| Category | CARC Code Range | Example | Recommended Action |\n|----------|-----------------|---------|-------------------|\n| Medical necessity not met | 50, 56 | Diagnosis does not support service | Appeal with clinical evidence |\n| Step therapy not completed | 149 | Required prior drug not tried | Document prior treatments |\n| Not a covered benefit | 96, 97 | Service excluded from plan | Formulary exception or plan change |\n| Documentation insufficient | 16, 252 | Missing clinical notes | Resubmit with complete records |\n| Experimental\u002Finvestigational | 56 | Off-label use not approved | Cite peer-reviewed evidence |\n| Out of network | 151 | Provider not in network | Network exception or referral |\n| Duplicate authorization | 18 | PA already exists for service | Verify existing PA status |\n\n## 11. PA Turnaround Time Requirements\n\n| Request Type | Commercial (typical) | Medicare Part C | Medicare Part D |\n|-------------|---------------------|-----------------|-----------------|\n| Standard (non-urgent) | 15 calendar days | 14 calendar days | 72 hours (standard) |\n| Expedited (urgent) | 72 hours | 72 hours | 24 hours |\n| Retrospective | 30 calendar days | 30 calendar days | N\u002FA |\n| Extension (pend for info) | +14 days (one extension) | +14 days | +14 days |\n\n### Urgency Determination Rules\n\nA request qualifies as **expedited** when:\n\n1. Applying standard timeframe could seriously jeopardize the patient's life or health\n2. Applying standard timeframe could jeopardize the patient's ability to regain maximum function\n3. A physician indicates the request is urgent (physician attestation)\n4. The patient is currently undergoing treatment that would be interrupted\n\n## 12. Quantity Limits and Site-of-Care Policies\n\n### 12.1 Quantity Limit Structures\n\n| Limit Type | Description | Example |\n|-----------|-------------|---------|\n| Per-fill limit | Maximum units per prescription fill | 30-day supply for controlled substances |\n| Per-period limit | Maximum units over a time period | 9 fills per year for triptan medications |\n| Lifetime limit | Maximum total units ever | Gene therapy — single administration |\n| Diagnosis-based limit | Quantity varies by condition | Higher opioid limits for cancer pain |\n\n### 12.2 Site-of-Care Optimization\n\nPayers increasingly require lower-cost sites for infusion and injection therapies:\n\n```\nIs the drug available for home infusion?\n├── YES → Home infusion preferred (lowest cost)\n│   ├── Patient clinically stable? → Approve home infusion\n│   └── Patient requires monitoring? → Approve outpatient infusion center\n└── NO → Is outpatient infusion center available?\n    ├── YES → Outpatient preferred over hospital outpatient\n    └── NO → Hospital outpatient department approved\n```\n\n## 13. Regulatory Timeline: Key PA Reform Dates\n\n| Date | Regulation | Impact |\n|------|-----------|--------|\n| Jan 2024 | CMS Interoperability Rule (CMS-0057-F) finalized | Payers must implement FHIR PAS API |\n| Jan 2026 | FHIR PAS API mandate effective | Payers must accept electronic PA via Da Vinci PAS |\n| Jan 2026 | PA decision transparency | Payers must provide specific denial reasons and applicable criteria |\n\n### Gold Carding Programs\n\nSeveral states require payers to exempt providers from PA if they demonstrate ≥90% approval rate for a specific service over 12 months. Exemption lasts 12 months, subject to audit, and is revoked if approval rate drops below threshold.\n",{"data":54,"body":63},{"name":4,"description":6,"usage":55,"version":56,"tags":57},"Use when evaluating prior authorization clinical policies, coverage rules, or appeal strategies.","1.0.0",[58,59,60,61,62],"skill","category:reasoning","prior-authorization","clinical-policy","hcls",{"type":64,"children":65},"root",[66,75,82,88,94,114,120,126,154,160,165,240,246,253,258,311,317,458,464,470,475,488,494,565,571,718,724,730,847,853,858,930,936,942,985,991,997,1098,1104,1214,1220,1225,1288,1294,1370,1376,1382,1387,1393,1511,1517,1535,1541,1559,1565,1789,1795,1804,1810,2002,2008,2129,2135,2147,2170,2176,2182,2280,2286,2291,2300,2306,2386,2392],{"type":67,"tag":68,"props":69,"children":71},"element","h1",{"id":70},"prior-authorization-clinical-policy-reasoning",[72],{"type":73,"value":74},"text","Prior Authorization Clinical Policy Reasoning",{"type":67,"tag":76,"props":77,"children":79},"h2",{"id":78},"overview",[80],{"type":73,"value":81},"Overview",{"type":67,"tag":83,"props":84,"children":85},"p",{},[86],{"type":73,"value":87},"Guide the agent through structured evaluation of prior authorization (PA) clinical policies,\ncoverage determinations, step therapy protocols, and appeals processes. This skill encodes\npayer policy logic so the agent can assess whether a requested service meets authorization\ncriteria, identify documentation gaps, and recommend appeal strategies.",{"type":67,"tag":76,"props":89,"children":91},{"id":90},"usage",[92],{"type":73,"value":93},"Usage",{"type":67,"tag":95,"props":96,"children":97},"ul",{},[98,104,109],{"type":67,"tag":99,"props":100,"children":101},"li",{},[102],{"type":73,"value":103},"Invoke when evaluating whether a service meets prior authorization clinical criteria",{"type":67,"tag":99,"props":105,"children":106},{},[107],{"type":73,"value":108},"Use for step therapy evaluation, medical necessity assessment, or appeal strategy",{"type":67,"tag":99,"props":110,"children":111},{},[112],{"type":73,"value":113},"Activate for CMS LCD\u002FNCD coverage rules or Da Vinci PAS implementation guidance",{"type":67,"tag":76,"props":115,"children":117},{"id":116},"core-concepts",[118],{"type":73,"value":119},"Core Concepts",{"type":67,"tag":76,"props":121,"children":123},{"id":122},"response-format",[124],{"type":73,"value":125},"Response Format",{"type":67,"tag":95,"props":127,"children":128},{},[129,134,139,144,149],{"type":67,"tag":99,"props":130,"children":131},{},[132],{"type":73,"value":133},"Lead with the direct recommendation or classification (≤3 sentences)",{"type":67,"tag":99,"props":135,"children":136},{},[137],{"type":73,"value":138},"Structure as: recommendation → justification (citing specific criteria\u002Fthresholds) → caveats",{"type":67,"tag":99,"props":140,"children":141},{},[142],{"type":73,"value":143},"Use tables for comparisons; bullet points for criteria lists",{"type":67,"tag":99,"props":145,"children":146},{},[147],{"type":73,"value":148},"Omit background the user already knows — they asked the question",{"type":67,"tag":99,"props":150,"children":151},{},[152],{"type":73,"value":153},"Target: 200-400 words unless the user requests exhaustive detail\nThe decision trees and frameworks in this skill are for internal reasoning only. Apply them to reach your conclusion, but do not reproduce them in your response. Present only the final recommendation with supporting evidence.",{"type":67,"tag":76,"props":155,"children":157},{"id":156},"_1-prior-authorization-decision-framework",[158],{"type":73,"value":159},"1. 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Triggers include \"discover a biomarker\", \"validate biomarker\", \"prognostic vs predictive\", \"feature selection\", \"LASSO vs elastic net\", \"nested cross-validation\", \"data leakage\", \"C-index\", \"time-dependent AUC\", \"decision curve analysis\", \"external validation cohort\", \"events per variable\", \"optimism-corrected\", \"multi-omics integration\", \"clinical utility of a biomarker\", \"is this biomarker ready\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2424,2425,2428,2429],{"name":2409,"slug":2410,"type":16},{"name":2426,"slug":2427,"type":16},"Bioinformatics","bioinformatics",{"name":2413,"slug":44,"type":16},{"name":2430,"slug":2431,"type":16},"Research","research","2026-07-12T08:37:49.295301",{"slug":2434,"name":2434,"fn":2435,"description":2436,"org":2437,"tags":2438,"stars":29,"repoUrl":30,"updatedAt":2446},"cdisc-compliance","reason about CDISC SDTM and ADaM implementation","Reason about CDISC SDTM and ADaM implementation for regulatory submissions. Use when the user asks about SDTM domain mapping, ADaM dataset design, controlled terminology versioning, define.xml completeness, FDA or PMDA submission requirements, query prioritization by clinical impact, SUPPQUAL usage, or CDISC compliance review. Triggers include \"SDTM mapping\", \"ADaM dataset\", \"CDISC compliance\", \"controlled terminology\", \"define.xml\", \"FDA submission data\", \"PMDA submission\", \"SDTM domain\", \"ADSL\", \"ADAE\", \"ADLB\", \"BDS structure\", \"SUPPQUAL\", \"RELREC\", \"value-level metadata\", \"CDISC CT\", \"regulatory submission data standards\", \"eCTD datasets\", \"SDTM 3.3\", \"ADaM 1.1\", \"query prioritization\", \"clinical data review\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2439,2442,2443],{"name":2440,"slug":2441,"type":16},"Clinical Trials","clinical-trials",{"name":2413,"slug":44,"type":16},{"name":2444,"slug":2445,"type":16},"Regulatory Compliance","regulatory-compliance","2026-07-12T08:37:33.35594",{"slug":2448,"name":2448,"fn":2449,"description":2450,"org":2451,"tags":2452,"stars":29,"repoUrl":30,"updatedAt":2461},"cell-type-annotation","annotate single-cell RNA-seq clusters","Generate code to assign cell type labels to single-cell RNA-seq clusters using CellTypist, SingleR, marker-based annotation, or reference label transfer (scANVI\u002Fingest). Triggers on requests to \"annotate cell types\", \"label clusters\", \"run CellTypist\", \"SingleR annotation\", \"marker gene dotplot\", \"transfer labels from reference atlas\", \"cell identity\", \"automated annotation\", \"reference mapping\", \"scANVI label transfer\", \"canonical markers\", \"immune cell types\", \"hierarchical annotation\", \"majority voting CellTypist\", \"over-clustering annotation\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2453,2454,2457,2458],{"name":2426,"slug":2427,"type":16},{"name":2455,"slug":2456,"type":16},"Data Analysis","data-analysis",{"name":2413,"slug":44,"type":16},{"name":2459,"slug":2460,"type":16},"RNA-seq","rna-seq","2026-07-12T08:38:05.443454",{"slug":2463,"name":2463,"fn":2464,"description":2465,"org":2466,"tags":2467,"stars":29,"repoUrl":30,"updatedAt":2474},"cheminformatics","calculate molecular properties with RDKit","Cheminformatics pipeline for small-molecule property calculation, filtering, and similarity analysis using RDKit. Use when the user asks to compute molecular descriptors, filter compounds by Lipinski or Veber rules, detect PAINS, calculate fingerprint similarity, run matched molecular pair analysis, generate ADMET descriptors, or process SMILES. Triggers include \"RDKit\", \"molecular descriptors\", \"Lipinski\", \"rule of five\", \"Veber\", \"PAINS\", \"pan-assay interference\", \"Morgan fingerprint\", \"Tanimoto\", \"fingerprint similarity\", \"matched molecular pair\", \"MMP\", \"mmpdb\", \"ADMET\", \"druglikeness\", \"SMILES\", \"cheminformatics\", \"compound filtering\", \"chemical similarity\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2468,2469,2472,2473],{"name":2426,"slug":2427,"type":16},{"name":2470,"slug":2471,"type":16},"Chemistry","chemistry",{"name":2455,"slug":2456,"type":16},{"name":2430,"slug":2431,"type":16},"2026-07-12T08:37:28.334619",{"slug":2476,"name":2476,"fn":2477,"description":2478,"org":2479,"tags":2480,"stars":29,"repoUrl":30,"updatedAt":2485},"claims-analytics","analyze and parse healthcare claims data","Pipeline skill for healthcare claims data parsing, analysis, and fraud detection. Use when the user asks to parse X12 837 or 835 claim files, manipulate ICD-10 CPT or HCPCS codes, detect billing pattern anomalies, profile providers against specialty peers, identify outlier billing behavior, validate NCCI edits programmatically, detect duplicate claims, run Benford's law analysis on charges, build claims data pipelines, or analyze E&M code distributions. Triggers include \"parse X12 837\", \"parse 835\", \"claims SQL\", \"ICD-10 manipulation\", \"CPT code analysis\", \"provider profiling\", \"billing outlier\", \"NCCI validation code\", \"duplicate claim detection\", \"Benford's law charges\", \"claims ETL\", \"E&M distribution analysis\", \"claims analytics pipeline\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2481,2482,2483,2484],{"name":2455,"slug":2456,"type":16},{"name":18,"slug":19,"type":16},{"name":24,"slug":25,"type":16},{"name":2413,"slug":44,"type":16},"2026-07-12T08:37:34.815088",{"slug":2487,"name":2487,"fn":2488,"description":2489,"org":2490,"tags":2491,"stars":29,"repoUrl":30,"updatedAt":2495},"claims-billing-rules","analyze healthcare claims billing rules","Reasoning skill for healthcare claims billing rules and fraud detection logic. Use when the user asks about CMS billing rules, place of service codes, global surgery periods, modifier usage (25 59 76 77), NCCI edit logic, column 1 column 2 code pairs, mutually exclusive procedures, modifier indicators, fraud waste and abuse patterns, E&M upcoding, unbundling, phantom billing, impossible day detection, coding error versus fraud distinction, FWA investigation methodology, or claims audit logic. Triggers include \"CMS billing rules\", \"NCCI edits\", \"modifier 25\", \"modifier 59\", \"global surgery period\", \"upcoding\", \"unbundling\", \"phantom billing\", \"impossible day\", \"FWA\", \"fraud waste abuse\", \"coding error vs fraud\", \"claims audit\", \"billing compliance\", \"E&M level selection\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2492,2493,2494],{"name":18,"slug":19,"type":16},{"name":24,"slug":25,"type":16},{"name":2444,"slug":2445,"type":16},"2026-07-12T08:38:28.210856",40,{"items":2498,"total":2674},[2499,2518,2539,2549,2562,2575,2585,2595,2616,2631,2646,2661],{"slug":2500,"name":2500,"fn":2501,"description":2502,"org":2503,"tags":2504,"stars":2515,"repoUrl":2516,"updatedAt":2517},"agentcore-investigation","investigate Bedrock AgentCore runtime sessions","Investigate Bedrock AgentCore runtime sessions via CloudWatch Logs Insights — resolve session\u002Ftrace IDs, query OTEL spans, filter noise, build timelines. Use when debugging AgentCore agent sessions, tracing tool calls, or analyzing latency.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2505,2506,2509,2512],{"name":2409,"slug":2410,"type":16},{"name":2507,"slug":2508,"type":16},"Debugging","debugging",{"name":2510,"slug":2511,"type":16},"Logs","logs",{"name":2513,"slug":2514,"type":16},"Observability","observability",9427,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":2519,"name":2520,"fn":2521,"description":2522,"org":2523,"tags":2524,"stars":2515,"repoUrl":2516,"updatedAt":2538},"amazon-aurora-dsql","amazon aurora dsql","build applications with Aurora DSQL","Build with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, FK replacement code generation, OCC retry patterns, ORM migration (Django\u002FHibernate\u002FRails), DDL operations, query plan explainability, SQL compatibility validation, and bulk data loading. Triggers on phrases like: DSQL, Aurora DSQL, create DSQL table, DSQL schema, migrate to DSQL, distributed SQL database, serverless PostgreSQL-compatible database, DSQL query plan, DSQL EXPLAIN ANALYZE, why is my DSQL query slow, DSQL foreign key, DSQL OCC retry, DSQL multi-region, load into DSQL, load CSV into DSQL, bulk load DSQL, aurora-dsql-loader.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2525,2528,2529,2532,2535],{"name":2526,"slug":2527,"type":16},"Aurora","aurora",{"name":2409,"slug":2410,"type":16},{"name":2530,"slug":2531,"type":16},"Database","database",{"name":2533,"slug":2534,"type":16},"Serverless","serverless",{"name":2536,"slug":2537,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":2540,"name":2541,"fn":2521,"description":2522,"org":2542,"tags":2543,"stars":2515,"repoUrl":2516,"updatedAt":2548},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2544,2545,2546,2547],{"name":2409,"slug":2410,"type":16},{"name":2530,"slug":2531,"type":16},{"name":2533,"slug":2534,"type":16},{"name":2536,"slug":2537,"type":16},"2026-07-12T08:36:42.694299",{"slug":2550,"name":2551,"fn":2521,"description":2522,"org":2552,"tags":2553,"stars":2515,"repoUrl":2516,"updatedAt":2561},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2554,2555,2556,2559,2560],{"name":2409,"slug":2410,"type":16},{"name":2530,"slug":2531,"type":16},{"name":2557,"slug":2558,"type":16},"Migration","migration",{"name":2533,"slug":2534,"type":16},{"name":2536,"slug":2537,"type":16},"2026-07-12T08:36:38.584057",{"slug":2563,"name":2564,"fn":2521,"description":2522,"org":2565,"tags":2566,"stars":2515,"repoUrl":2516,"updatedAt":2574},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2567,2568,2569,2572,2573],{"name":2409,"slug":2410,"type":16},{"name":2530,"slug":2531,"type":16},{"name":2570,"slug":2571,"type":16},"PostgreSQL","postgresql",{"name":2533,"slug":2534,"type":16},{"name":2536,"slug":2537,"type":16},"2026-07-12T08:36:46.530743",{"slug":2576,"name":2577,"fn":2521,"description":2522,"org":2578,"tags":2579,"stars":2515,"repoUrl":2516,"updatedAt":2584},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2580,2581,2582,2583],{"name":2409,"slug":2410,"type":16},{"name":2530,"slug":2531,"type":16},{"name":2533,"slug":2534,"type":16},{"name":2536,"slug":2537,"type":16},"2026-07-12T08:36:48.104182",{"slug":2586,"name":2586,"fn":2521,"description":2522,"org":2587,"tags":2588,"stars":2515,"repoUrl":2516,"updatedAt":2594},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2589,2590,2591,2592,2593],{"name":2409,"slug":2410,"type":16},{"name":2530,"slug":2531,"type":16},{"name":2557,"slug":2558,"type":16},{"name":2533,"slug":2534,"type":16},{"name":2536,"slug":2537,"type":16},"2026-07-12T08:36:36.374512",{"slug":2596,"name":2596,"fn":2597,"description":2598,"org":2599,"tags":2600,"stars":2613,"repoUrl":2614,"updatedAt":2615},"cost-efficiency-analyzer","analyze cost efficiency and expenses","Analyzes cost structure, cost efficiency, and expense management from P&L data. Use when the user asks about costs, expenses, COGS, operating expenses, cost ratios, cost control, spending efficiency, margin compression from cost side, or wants to understand where money is going. Also use for \"are we spending too much\", \"cost breakdown\", \"expense analysis\", or \"how efficient are our operations\". NOT for revenue or top-line analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2601,2604,2607,2610],{"name":2602,"slug":2603,"type":16},"Accounting","accounting",{"name":2605,"slug":2606,"type":16},"Analytics","analytics",{"name":2608,"slug":2609,"type":16},"Cost Optimization","cost-optimization",{"name":2611,"slug":2612,"type":16},"Finance","finance",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",{"slug":2617,"name":2617,"fn":2618,"description":2619,"org":2620,"tags":2621,"stars":2613,"repoUrl":2614,"updatedAt":2630},"executive-financial-briefing","generate executive financial briefings","Generates a concise executive-level financial briefing or summary suitable for a CEO, CFO, or board presentation. Use when the user asks for a summary, briefing, executive summary, board update, financial overview, financial health check, or \"how is the business doing\". Covers the full P&L picture in one page. Also use for \"give me the highlights\", \"what do I need to know\", or \"quick financial update\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2622,2623,2624,2627],{"name":2409,"slug":2410,"type":16},{"name":2611,"slug":2612,"type":16},{"name":2625,"slug":2626,"type":16},"Management","management",{"name":2628,"slug":2629,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":2632,"name":2632,"fn":2633,"description":2634,"org":2635,"tags":2636,"stars":2613,"repoUrl":2614,"updatedAt":2645},"multi-quarter-trend-analysis","analyze multi-quarter financial trends","Analyzes financial trends across multiple quarters by comparing P&L metrics over time. Use when the user wants to see trends, patterns, trajectories, or directional movement across 3 or more quarters. Also use for \"how are we trending\", \"show me the trend\", \"track performance over time\", \"quarter over quarter comparison across all quarters\", or any multi-period longitudinal analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2637,2638,2639,2642],{"name":2605,"slug":2606,"type":16},{"name":2611,"slug":2612,"type":16},{"name":2640,"slug":2641,"type":16},"Financial Statements","financial-statements",{"name":2643,"slug":2644,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":2647,"name":2647,"fn":2648,"description":2649,"org":2650,"tags":2651,"stars":2613,"repoUrl":2614,"updatedAt":2660},"pdf","process and manipulate PDF documents","Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text\u002Ftables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting\u002Fdecrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2652,2655,2658],{"name":2653,"slug":2654,"type":16},"Automation","automation",{"name":2656,"slug":2657,"type":16},"Documents","documents",{"name":2659,"slug":2647,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":2662,"name":2662,"fn":2663,"description":2664,"org":2665,"tags":2666,"stars":2613,"repoUrl":2614,"updatedAt":2673},"quarterly-kpi-calculator","calculate quarterly financial KPIs","Calculates quarterly financial KPIs from P&L data. P&L figures can be provided directly by the user or fetched from the financial data MCP server. Use when the user wants KPI calculations such as Gross Margin %, EBITDA Margin %, Operating Expense Ratio, or Revenue Growth % QoQ. Also use for quarterly performance review, P&L analysis, or interpreting financial ratios against benchmarks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2667,2668,2669,2670],{"name":2602,"slug":2603,"type":16},{"name":2455,"slug":2456,"type":16},{"name":2611,"slug":2612,"type":16},{"name":2671,"slug":2672,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",150]