[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-risk-adjustment-strategy":3,"mdc--c0b0xa-key":50,"related-org-aws-labs-risk-adjustment-strategy":2382,"related-repo-aws-labs-risk-adjustment-strategy":2564},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":45,"sourceUrl":48,"mdContent":49},"risk-adjustment-strategy","analyze CMS-HCC risk adjustment strategy","Reasoning skill for CMS-HCC risk adjustment strategy and methodology. Use when the user asks about CMS-HCC model versions V24 or V28, blended transition methodology, ICD-10-to-HCC mapping logic, disease interaction hierarchies, RAF score methodology, risk adjustment factor calculation, coding gap identification, audit-defensible documentation, HCC recapture strategy, prospective vs retrospective risk adjustment, or Medicare Advantage risk scoring. Triggers include \"CMS-HCC\", \"V24\", \"V28\", \"blended transition\", \"HCC mapping\", \"disease hierarchy\", \"RAF score\", \"risk adjustment\", \"coding gap\", \"HCC recapture\", \"chart review\", \"audit defensible\", \"risk score methodology\", \"Medicare Advantage risk\", \"capitation revenue\", \"hierarchical condition category\".\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],{"name":14,"slug":15,"type":16},"Research","research","tag",{"name":18,"slug":19,"type":16},"Compliance","compliance",{"name":21,"slug":22,"type":16},"Healthcare","healthcare",{"name":24,"slug":25,"type":16},"Insurance","insurance",4,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fhcls-agent-skills","2026-07-12T08:38:30.154711",null,0,[32,33,34,35,36,37,38,39,40,41,42,43,44],"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":27,"stars":26,"forks":30,"topics":46,"description":47},[32,33,34,35,36,37,38,39,40,41,42,43,44],"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\u002Frisk-adjustment-strategy","---\nname: risk-adjustment-strategy\ndescription: >\n  Reasoning skill for CMS-HCC risk adjustment strategy and methodology. Use when the user asks\n  about CMS-HCC model versions V24 or V28, blended transition methodology, ICD-10-to-HCC\n  mapping logic, disease interaction hierarchies, RAF score methodology, risk adjustment factor\n  calculation, coding gap identification, audit-defensible documentation, HCC recapture strategy,\n  prospective vs retrospective risk adjustment, or Medicare Advantage risk scoring. Triggers\n  include \"CMS-HCC\", \"V24\", \"V28\", \"blended transition\", \"HCC mapping\", \"disease hierarchy\",\n  \"RAF score\", \"risk adjustment\", \"coding gap\", \"HCC recapture\", \"chart review\", \"audit\n  defensible\", \"risk score methodology\", \"Medicare Advantage risk\", \"capitation revenue\",\n  \"hierarchical condition category\".\nusage: Invoke when reasoning about CMS-HCC risk adjustment strategy, model version selection, coding gap identification, or audit-defensible documentation requirements.\nversion: 1.0.0\ntags: [skill, category:reasoning, risk-adjustment, hcc, hcls]\n---\n\n# Risk Adjustment Strategy — Reasoning Skill\n\n## Overview\n\nGuide the agent through CMS-HCC risk adjustment methodology, model version differences,\nhierarchy resolution, coding gap identification, and audit-defensible documentation\nrequirements. This skill encodes regulatory and actuarial knowledge for risk adjustment\nprograms, primarily Medicare Advantage (Part C).\n\n## Usage\n\n- Reason about CMS-HCC model versions (V24\u002FV28), blended transition, and hierarchy resolution\n- Identify coding gaps and design audit-defensible recapture strategies\n\n## Core Concepts\n\n---\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. CMS-HCC Model Overview\n\n### What Is Risk Adjustment?\n\nCMS pays Medicare Advantage (MA) plans a capitated amount per member per month. The payment\nis adjusted based on the member's health status, measured by diagnoses submitted on claims.\nSicker members generate higher payments (higher RAF scores). The CMS-HCC model translates\nICD-10-CM diagnosis codes into Hierarchical Condition Categories (HCCs) and calculates a\nRisk Adjustment Factor (RAF) score.\n\n### RAF Score Formula\n\n```\nRAF = demographic_coefficient\n    + SUM(hcc_coefficients for all active HCCs after hierarchy resolution)\n    + SUM(disease_interaction_terms)\n    + coding_intensity_adjustment (negative, applied by CMS)\n```\n\n### Model Versions\n\n| Attribute | V24 (Legacy) | V28 (Current) |\n|---|---|---|\n| Number of HCCs | 86 | 115 |\n| Payment year | Through 2023 (blended 2024–2025) | 2026+ (full weight) |\n| Key additions | — | Substance use disorders, social determinants proxies, expanded mental health |\n| Key removals | — | Some lower-severity HCCs consolidated |\n| Coefficient source | 2015–2016 FFS data | 2017–2018 FFS data |\n| Coding intensity adj. | -5.90% (2024) | Built into coefficients |\n\n### Blended Transition Schedule (2024–2025)\n\n| Payment Year | V24 Weight | V28 Weight |\n|---|---|---|\n| 2024 | 67% | 33% |\n| 2025 | 33% | 67% |\n| 2026+ | 0% | 100% |\n\n**Implication**: During the transition, BOTH models must be run and blended. A diagnosis that\nmaps to an HCC in V24 but not V28 (or vice versa) has partial payment impact.\n\n---\n\n## 2. ICD-10-to-HCC Mapping\n\n### Mapping Pipeline\n\n```\nICD-10-CM code\n  → CMS crosswalk → Condition Category (CC)\n    → Hierarchy resolution → HCC (only highest-severity CC in each hierarchy retained)\n      → Coefficient lookup → RAF contribution\n```\n\n### Mapping Rules\n\n1. **Only specific ICD-10 codes map to CCs**. Many ICD-10 codes have no CC mapping and\n   contribute nothing to the RAF score.\n2. **Multiple ICD-10 codes can map to the same CC**. Any one qualifying code is sufficient.\n3. **CCs are grouped into hierarchies**. Within each hierarchy, only the highest-severity CC\n   is retained as an HCC.\n4. **Diagnoses must be from face-to-face encounters** with acceptable provider types\n   (physician, NP, PA, etc.). Lab-only or radiology-only encounters do NOT qualify.\n5. **Diagnoses must be submitted annually**. HCCs do not carry forward year-to-year\n   (except for certain conditions in the ESRD model).\n\n### Example Hierarchy: Diabetes\n\n| CC | Description | Hierarchy Position | V24 Coefficient (Community, Non-Dual, Aged) |\n|---|---|---|---|\n| 17 | Diabetes with Acute Complications | Highest | 0.368 |\n| 18 | Diabetes with Chronic Complications | ↓ | 0.368 |\n| 19 | Diabetes without Complication | Lowest | 0.118 |\n\n**Rule**: If a member has both CC 17 and CC 19, only CC 17 (HCC 17) is retained. CC 19 is\n\"hierarchied off.\"\n\n### Common Hierarchies to Know\n\n| Hierarchy Group | CCs (V24, highest → lowest) | Clinical Area |\n|---|---|---|\n| Diabetes | 17 → 18 → 19 | Endocrine |\n| Heart Failure | 85 → 86 → 87 | Cardiovascular |\n| COPD | 111 → 112 | Pulmonary |\n| Renal | 136 → 137 → 138 | Nephrology |\n| Cancer | 8 → 9 → 10 → 11 → 12 | Oncology |\n| Vascular | 107 → 108 | Cardiovascular |\n\n---\n\n## 3. Disease Interaction Terms\n\n### What Are Interactions?\n\nCMS recognizes that certain combinations of HCCs have a greater-than-additive cost impact.\nInteraction terms add additional RAF points when specific HCC pairs (or groups) co-occur.\n\n### Key Interaction Terms (V24)\n\n| Interaction | HCCs Required | Additional Coefficient |\n|---|---|---|\n| Diabetes + CHF | HCC 17\u002F18 + HCC 85\u002F86 | +0.154 |\n| CHF + COPD | HCC 85\u002F86 + HCC 111\u002F112 | +0.175 |\n| CHF + Renal | HCC 85\u002F86 + HCC 136\u002F137\u002F138 | +0.154 |\n| Diabetes + CHF + COPD | HCC 17\u002F18 + HCC 85\u002F86 + HCC 111\u002F112 | +0.047 (additional) |\n| Cancer + Immune | HCC 8\u002F9\u002F10\u002F11\u002F12 + HCC 47 | +0.190 |\n\n**Rule**: Interaction terms are ONLY applied if the component HCCs survive hierarchy\nresolution. If a higher-severity CC in the same hierarchy replaces a component, re-check\nwhether the interaction still qualifies.\n\n---\n\n## 4. Coding Gap Identification\n\n### What Is a Coding Gap?\n\nA coding gap exists when clinical evidence suggests a condition is present, but no qualifying\nICD-10 code has been submitted on a face-to-face claim in the current payment year.\n\n### Identification Methods\n\n#### 4a. Rx Proxy Analysis\n\n| Medication (Rx) | Suspected Condition | Target HCC (V24) |\n|---|---|---|\n| Metformin, glipizide, insulin | Diabetes mellitus | HCC 19 (or 17\u002F18 with complications) |\n| Statins (atorvastatin, rosuvastatin) | Hyperlipidemia | No HCC (but may indicate vascular disease) |\n| Lisinopril, losartan | Hypertension | No HCC (but may indicate CHF, renal disease) |\n| Furosemide, spironolactone | Heart failure | HCC 85\u002F86\u002F87 |\n| Albuterol, fluticasone\u002Fsalmeterol | COPD or asthma | HCC 111\u002F112 |\n| Donepezil, memantine | Dementia | HCC 51\u002F52 |\n| Warfarin, apixaban | Atrial fibrillation or DVT\u002FPE | HCC 96 or 107\u002F108 |\n| Methotrexate, adalimumab | Rheumatoid arthritis | HCC 40 |\n\n**Rule**: Rx proxies identify SUSPECTED gaps. They are NOT sufficient for coding — a provider\nmust document and code the condition on a qualifying encounter.\n\n#### 4b. Lab Proxy Analysis\n\n| Lab Result | Suspected Condition | Target HCC |\n|---|---|---|\n| HbA1c ≥ 6.5% | Diabetes mellitus | HCC 19+ |\n| eGFR \u003C 60 mL\u002Fmin | Chronic kidney disease | HCC 136\u002F137\u002F138 |\n| BNP > 100 pg\u002FmL | Heart failure | HCC 85\u002F86\u002F87 |\n| TSH > 10 mIU\u002FL | Hypothyroidism | No HCC |\n| BMI ≥ 40 | Morbid obesity | HCC 22 |\n\n#### 4c. Historical Diagnosis Analysis\n\nConditions documented in the prior year but not yet recaptured in the current year.\n\n**Decision tree for gap prioritization**:\n```\nIs the condition chronic (expected to persist year-over-year)?\n ├─ YES\n │   ├─ Was it documented in the prior year?\n │   │   ├─ YES → High-priority recapture gap\n │   │   └─ NO → New gap identified by Rx\u002Flab proxy\n │   └─ Does it map to an HCC?\n │       ├─ YES → Revenue-impacting gap → prioritize\n │       └─ NO → Clinical gap only → lower priority for risk adjustment\n └─ NO (acute condition)\n     └─ Do NOT assume recapture; only code if currently active\n```\n\n---\n\n## 5. Audit-Defensible Documentation\n\n### CMS Audit Requirements (RADV)\n\nCMS conducts Risk Adjustment Data Validation (RADV) audits to verify that submitted diagnoses\nare supported by medical record documentation.\n\n### Documentation Must Support\n\n1. **The specific ICD-10 code submitted** — not just the general condition category.\n2. **Face-to-face encounter** with an acceptable provider type.\n3. **Assessment, monitoring, evaluation, or treatment** of the condition during the encounter.\n4. **Date of service** matching the claim.\n5. **Provider signature** (or authenticated electronic signature).\n\n### Documentation Decision Tree\n\n```\nDoes the medical record contain:\n ├─ A face-to-face encounter note? → If NO, diagnosis is NOT audit-defensible\n ├─ Provider signature\u002Fauthentication? → If NO, not defensible\n ├─ The condition listed in the assessment\u002Fplan? → If NO, not defensible\n ├─ Evidence of evaluation or management of the condition?\n │   (e.g., medication review, test ordering, counseling)\n │   → If NO, not defensible (listing alone is insufficient)\n └─ Specificity matching the ICD-10 code?\n     (e.g., \"diabetes with nephropathy\" for E11.21, not just \"diabetes\")\n     → If NO, code must be downgraded to the supported specificity level\n```\n\n### Common Documentation Failures\n\n| Failure | Example | Risk |\n|---|---|---|\n| Problem list only | Diabetes on problem list but not addressed in note | HCC deleted on audit |\n| Cloned notes | Identical assessment across multiple visits | All HCCs at risk |\n| Unspecified codes | E11.9 (diabetes unspecified) when complications exist | Missed higher HCC |\n| Missing laterality | I63.511 vs I63.512 (stroke, right vs left) | Code rejected |\n| Resolved conditions | \"History of cancer\" coded as active cancer | HCC deleted + penalty |\n| Missing provider type | Diagnosis from lab-only encounter | Does not qualify |\n\n---\n\n## 6. Recapture Strategy\n\n### Annual Recapture Workflow\n\n1. **Identify prior-year HCCs**: Pull all HCCs from the prior payment year.\n2. **Check current-year claims**: Which HCCs have already been recaptured?\n3. **Flag gaps**: Prior-year HCCs not yet recaptured = recapture opportunities.\n4. **Prioritize by RAF impact**: Sort gaps by coefficient value (highest first).\n5. **Schedule encounters**: Coordinate with care management to ensure face-to-face visits\n   address open gaps.\n6. **Validate documentation**: After encounter, verify the note supports the specific ICD-10 code.\n\n### Recapture Prioritization Matrix\n\n| Priority | Criteria | Action |\n|---|---|---|\n| Critical | HCC coefficient > 0.3 AND chronic condition | Schedule dedicated visit or ensure addressed at next visit |\n| High | HCC coefficient 0.15–0.3 AND chronic | Address at next scheduled visit |\n| Medium | HCC coefficient \u003C 0.15 AND chronic | Address opportunistically |\n| Low | Acute condition from prior year | Do NOT recapture unless condition is still active |\n\n### Timing Considerations\n\n| Quarter | Strategy |\n|---|---|\n| Q1 (Jan–Mar) | Begin recapture for highest-value HCCs; schedule annual wellness visits |\n| Q2 (Apr–Jun) | Mid-year gap report; target members with no visits yet |\n| Q3 (Jul–Sep) | Escalate outreach for members with open high-value gaps |\n| Q4 (Oct–Dec) | Final sweep; focus on members with scheduled visits remaining |\n\n---\n\n## 7. Model Selection During Transition\n\n### Decision Framework for 2024–2025\n\n```\nWhich model version should I optimize for?\n ├─ Payment year 2024?\n │   └─ Run BOTH V24 and V28. Weight: 67% V24 + 33% V28.\n │       Focus on V24 HCCs (higher weight) but do not ignore V28-only HCCs.\n ├─ Payment year 2025?\n │   └─ Run BOTH. Weight: 33% V24 + 67% V28.\n │       Shift focus to V28 HCCs.\n └─ Payment year 2026+?\n     └─ V28 only.\n```\n\n### HCCs That Changed Between V24 and V28\n\n| Change Type | Example | Impact |\n|---|---|---|\n| Removed in V28 | Some lower-severity CCs consolidated | Lost revenue if only V28 applies |\n| New in V28 | Substance use disorders, expanded mental health | New revenue opportunity |\n| Coefficient changed | Diabetes coefficients recalibrated | May increase or decrease RAF |\n| Hierarchy restructured | Some hierarchies split or merged | Different CC may survive hierarchy |\n\n**Rule**: During the transition, a diagnosis that maps to an HCC in V24 but NOT V28 still\nhas partial value (67% in 2024, 33% in 2025). Do not ignore these diagnoses.\n\n---\n\n## When NOT to Use This Skill\n\n- Submitting RAF scores to CMS (needs certified risk adjustment coder)\n- When chart review findings contradict claims-based HCC assignments\n- Individual member clinical documentation (needs provider engagement)\n\n## When to Escalate to a Human Expert\n\n- When audit identifies systematic upcoding patterns requiring compliance review\n- Before extrapolating risk scores to populations outside the model's training data\n- When V24\u002FV28 transition creates >5% revenue impact requiring actuarial review\n\n## 8. Common Mistakes\n\n1. **Wrong:** Coding diagnoses directly from Rx claims without a face-to-face encounter\n   **Right:** Use Rx data to identify suspected gaps, then document the condition via a qualifying face-to-face encounter\n   **Why:** Rx claims are proxies, not diagnosis sources — CMS requires face-to-face documentation for risk adjustment\n\n2. **Wrong:** Recapturing conditions that have resolved (e.g., coding \"history of cancer\" as active cancer)\n   **Right:** Only code conditions that are currently active and being evaluated, monitored, or treated\n   **Why:** \"History of\" is not an active diagnosis; submitting resolved conditions as active is audit-indefensible and may constitute fraud\n\n3. **Wrong:** Calculating RAF scores without applying hierarchy resolution\n   **Right:** Always resolve hierarchies (retain only the highest-severity CC in each group) before summing coefficients\n   **Why:** Counting superseded CCs overstates the RAF score and produces incorrect revenue projections\n\n4. **Wrong:** Optimizing coding strategy for V24 only during the 2025 payment year\n   **Right:** Run both V24 and V28 models with appropriate blend weights (33% V24 \u002F 67% V28 in 2025)\n   **Why:** V28 carries 67% weight in 2025; ignoring it means missing the majority of payment impact\n\n5. **Wrong:** Submitting diagnoses from lab-only or radiology-only encounters\n   **Right:** Ensure every submitted diagnosis comes from a face-to-face encounter with a qualifying provider type (MD, DO, NP, PA)\n   **Why:** Lab-only encounters do not qualify for risk adjustment under CMS rules — these diagnoses will be deleted on RADV audit\n\n6. **Wrong:** Cloning documentation across multiple visits with identical assessment text\n   **Right:** Ensure each encounter note reflects the specific visit with unique clinical details and current status\n   **Why:** Cloned notes put all associated HCCs at risk during audit — CMS may delete every HCC from cloned documentation\n\n7. **Wrong:** Submitting unspecified ICD-10 codes when clinical documentation supports higher specificity\n   **Right:** Code to the highest specificity level supported by the documentation (e.g., E11.21 instead of E11.9)\n   **Why:** Unspecified codes may map to a lower-value HCC or no HCC at all, leaving revenue on the table\n\n---\n\n## 9. Reporting Checklist\n\nEvery risk adjustment analysis MUST report:\n\n1. **Model version(s)**: V24, V28, or blended (with weights).\n2. **Population**: Medicare Advantage, Medicaid, ACA marketplace.\n3. **Payment year**: Determines which model version and coefficients apply.\n4. **Hierarchy resolution**: Confirm hierarchies were applied before RAF calculation.\n5. **Interaction terms**: List which interactions were evaluated and applied.\n6. **Coding gap methodology**: Rx proxy, lab proxy, historical, or chart review.\n7. **Audit readiness**: Documentation validation status for submitted HCCs.\n8. **Coding intensity adjustment**: Applied by CMS; note the current percentage.\n\n---\n\n## 10. Glossary\n\n| Term | Definition |\n|---|---|\n| CC | Condition Category — intermediate grouping of ICD-10 codes |\n| HCC | Hierarchical Condition Category — CC that survives hierarchy resolution |\n| RAF | Risk Adjustment Factor — numeric score representing expected cost |\n| RADV | Risk Adjustment Data Validation — CMS audit program |\n| MA | Medicare Advantage — Part C managed care plans |\n| FFS | Fee-for-Service — traditional Medicare payment model |\n| ESRD | End-Stage Renal Disease — separate CMS-HCC model |\n| AWV | Annual Wellness Visit — key encounter for HCC recapture |\n| NPI | National Provider Identifier |\n| PY | Payment Year — the calendar year for which RAF scores are calculated |\n",{"data":51,"body":59},{"name":4,"description":6,"usage":52,"version":53,"tags":54},"Invoke when reasoning about CMS-HCC risk adjustment strategy, model version selection, coding gap identification, or audit-defensible documentation requirements.","1.0.0",[55,56,43,57,58],"skill","category:reasoning","hcc","hcls",{"type":60,"children":61},"root",[62,71,78,84,90,105,111,115,121,149,155,162,167,173,186,192,332,338,417,428,431,437,443,452,458,512,518,618,628,634,768,771,777,783,788,794,910,919,922,928,934,939,945,952,1123,1132,1138,1251,1257,1262,1272,1281,1284,1290,1296,1301,1307,1360,1366,1375,1381,1516,1519,1525,1531,1594,1600,1699,1705,1779,1782,1788,1794,1803,1809,1907,1916,1919,1925,1943,1949,1967,1973,2126,2129,2135,2140,2222,2225,2231],{"type":63,"tag":64,"props":65,"children":67},"element","h1",{"id":66},"risk-adjustment-strategy-reasoning-skill",[68],{"type":69,"value":70},"text","Risk Adjustment Strategy — Reasoning Skill",{"type":63,"tag":72,"props":73,"children":75},"h2",{"id":74},"overview",[76],{"type":69,"value":77},"Overview",{"type":63,"tag":79,"props":80,"children":81},"p",{},[82],{"type":69,"value":83},"Guide the agent through CMS-HCC risk adjustment methodology, model version differences,\nhierarchy resolution, coding gap identification, and audit-defensible documentation\nrequirements. 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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},[2412,2415,2416,2419,2422],{"name":2413,"slug":2414,"type":16},"Aurora","aurora",{"name":2391,"slug":2392,"type":16},{"name":2417,"slug":2418,"type":16},"Database","database",{"name":2420,"slug":2421,"type":16},"Serverless","serverless",{"name":2423,"slug":2424,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":2427,"name":2428,"fn":2408,"description":2409,"org":2429,"tags":2430,"stars":2402,"repoUrl":2403,"updatedAt":2435},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2431,2432,2433,2434],{"name":2391,"slug":2392,"type":16},{"name":2417,"slug":2418,"type":16},{"name":2420,"slug":2421,"type":16},{"name":2423,"slug":2424,"type":16},"2026-07-12T08:36:42.694299",{"slug":2437,"name":2438,"fn":2408,"description":2409,"org":2439,"tags":2440,"stars":2402,"repoUrl":2403,"updatedAt":2448},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2441,2442,2443,2446,2447],{"name":2391,"slug":2392,"type":16},{"name":2417,"slug":2418,"type":16},{"name":2444,"slug":2445,"type":16},"Migration","migration",{"name":2420,"slug":2421,"type":16},{"name":2423,"slug":2424,"type":16},"2026-07-12T08:36:38.584057",{"slug":2450,"name":2451,"fn":2408,"description":2409,"org":2452,"tags":2453,"stars":2402,"repoUrl":2403,"updatedAt":2461},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2454,2455,2456,2459,2460],{"name":2391,"slug":2392,"type":16},{"name":2417,"slug":2418,"type":16},{"name":2457,"slug":2458,"type":16},"PostgreSQL","postgresql",{"name":2420,"slug":2421,"type":16},{"name":2423,"slug":2424,"type":16},"2026-07-12T08:36:46.530743",{"slug":2463,"name":2464,"fn":2408,"description":2409,"org":2465,"tags":2466,"stars":2402,"repoUrl":2403,"updatedAt":2471},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2467,2468,2469,2470],{"name":2391,"slug":2392,"type":16},{"name":2417,"slug":2418,"type":16},{"name":2420,"slug":2421,"type":16},{"name":2423,"slug":2424,"type":16},"2026-07-12T08:36:48.104182",{"slug":2473,"name":2473,"fn":2408,"description":2409,"org":2474,"tags":2475,"stars":2402,"repoUrl":2403,"updatedAt":2481},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2476,2477,2478,2479,2480],{"name":2391,"slug":2392,"type":16},{"name":2417,"slug":2418,"type":16},{"name":2444,"slug":2445,"type":16},{"name":2420,"slug":2421,"type":16},{"name":2423,"slug":2424,"type":16},"2026-07-12T08:36:36.374512",{"slug":2483,"name":2483,"fn":2484,"description":2485,"org":2486,"tags":2487,"stars":2500,"repoUrl":2501,"updatedAt":2502},"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},[2488,2491,2494,2497],{"name":2489,"slug":2490,"type":16},"Accounting","accounting",{"name":2492,"slug":2493,"type":16},"Analytics","analytics",{"name":2495,"slug":2496,"type":16},"Cost Optimization","cost-optimization",{"name":2498,"slug":2499,"type":16},"Finance","finance",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",{"slug":2504,"name":2504,"fn":2505,"description":2506,"org":2507,"tags":2508,"stars":2500,"repoUrl":2501,"updatedAt":2517},"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},[2509,2510,2511,2514],{"name":2391,"slug":2392,"type":16},{"name":2498,"slug":2499,"type":16},{"name":2512,"slug":2513,"type":16},"Management","management",{"name":2515,"slug":2516,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":2519,"name":2519,"fn":2520,"description":2521,"org":2522,"tags":2523,"stars":2500,"repoUrl":2501,"updatedAt":2532},"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},[2524,2525,2526,2529],{"name":2492,"slug":2493,"type":16},{"name":2498,"slug":2499,"type":16},{"name":2527,"slug":2528,"type":16},"Financial Statements","financial-statements",{"name":2530,"slug":2531,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":2534,"name":2534,"fn":2535,"description":2536,"org":2537,"tags":2538,"stars":2500,"repoUrl":2501,"updatedAt":2547},"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},[2539,2542,2545],{"name":2540,"slug":2541,"type":16},"Automation","automation",{"name":2543,"slug":2544,"type":16},"Documents","documents",{"name":2546,"slug":2534,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":2549,"name":2549,"fn":2550,"description":2551,"org":2552,"tags":2553,"stars":2500,"repoUrl":2501,"updatedAt":2562},"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},[2554,2555,2558,2559],{"name":2489,"slug":2490,"type":16},{"name":2556,"slug":2557,"type":16},"Data Analysis","data-analysis",{"name":2498,"slug":2499,"type":16},{"name":2560,"slug":2561,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",150,{"items":2565,"total":2657},[2566,2583,2596,2610,2623,2636,2647],{"slug":2567,"name":2567,"fn":2568,"description":2569,"org":2570,"tags":2571,"stars":26,"repoUrl":27,"updatedAt":2582},"aws-genai-ml-architect","design AWS GenAI and ML architectures","Reasoning skill for designing AWS GenAI and ML architectures for healthcare and life sciences workloads. Use when the user asks to choose between SageMaker and Bedrock, design a RAG system over medical literature, architect clinical NLP or medical imaging inference, plan genomics or drug discovery pipelines on AWS, address HIPAA\u002FPHI compliance in ML systems, design MLOps for regulated clinical models, or optimize cost for HCLS ML workloads. Triggers include \"AWS architecture\", \"SageMaker vs Bedrock\", \"HIPAA ML\", \"clinical RAG\", \"medical imaging inference\", \"genomics on AWS\", \"PHI training\", \"MLOps healthcare\", \"Bedrock guardrails\", \"HealthLake\", \"HCLS cloud architecture\", \"BAA compliance\", \"SageMaker endpoint\", \"Bedrock knowledge base\", \"clinical NLP on AWS\", \"FDA SaMD on AWS\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[2572,2575,2576,2577,2579],{"name":2573,"slug":2574,"type":16},"Architecture","architecture",{"name":2391,"slug":2392,"type":16},{"name":21,"slug":22,"type":16},{"name":2578,"slug":41,"type":16},"Life Sciences",{"name":2580,"slug":2581,"type":16},"LLM","llm","2026-07-12T08:38:07.975937",{"slug":2584,"name":2584,"fn":2585,"description":2586,"org":2587,"tags":2588,"stars":26,"repoUrl":27,"updatedAt":2595},"biomarker-discovery","guide biomarker discovery and validation","Reason about biomarker discovery and validation in HCLS — classifying biomarker intent, choosing feature-selection and cross-validation strategies, avoiding leakage, and planning external replication. Use when the user asks to discover, develop, or validate a biomarker; select features from high-dimensional omics or clinical data; design a validation study; choose evaluation metrics; justify sample size; combine multi-omics signals; or assess clinical utility. 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},[2589,2590,2593,2594],{"name":2391,"slug":2392,"type":16},{"name":2591,"slug":2592,"type":16},"Bioinformatics","bioinformatics",{"name":2578,"slug":41,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T08:37:49.295301",{"slug":2597,"name":2597,"fn":2598,"description":2599,"org":2600,"tags":2601,"stars":26,"repoUrl":27,"updatedAt":2609},"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},[2602,2605,2606],{"name":2603,"slug":2604,"type":16},"Clinical Trials","clinical-trials",{"name":2578,"slug":41,"type":16},{"name":2607,"slug":2608,"type":16},"Regulatory Compliance","regulatory-compliance","2026-07-12T08:37:33.35594",{"slug":2611,"name":2611,"fn":2612,"description":2613,"org":2614,"tags":2615,"stars":26,"repoUrl":27,"updatedAt":2622},"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},[2616,2617,2618,2619],{"name":2591,"slug":2592,"type":16},{"name":2556,"slug":2557,"type":16},{"name":2578,"slug":41,"type":16},{"name":2620,"slug":2621,"type":16},"RNA-seq","rna-seq","2026-07-12T08:38:05.443454",{"slug":2624,"name":2624,"fn":2625,"description":2626,"org":2627,"tags":2628,"stars":26,"repoUrl":27,"updatedAt":2635},"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},[2629,2630,2633,2634],{"name":2591,"slug":2592,"type":16},{"name":2631,"slug":2632,"type":16},"Chemistry","chemistry",{"name":2556,"slug":2557,"type":16},{"name":14,"slug":15,"type":16},"2026-07-12T08:37:28.334619",{"slug":2637,"name":2637,"fn":2638,"description":2639,"org":2640,"tags":2641,"stars":26,"repoUrl":27,"updatedAt":2646},"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},[2642,2643,2644,2645],{"name":2556,"slug":2557,"type":16},{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"name":2578,"slug":41,"type":16},"2026-07-12T08:37:34.815088",{"slug":2648,"name":2648,"fn":2649,"description":2650,"org":2651,"tags":2652,"stars":26,"repoUrl":27,"updatedAt":2656},"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},[2653,2654,2655],{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"name":2607,"slug":2608,"type":16},"2026-07-12T08:38:28.210856",40]