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Use when the user asks about HEDIS measure definitions, denominator\u002Fnumerator\u002Fexclusion logic, continuous enrollment rules, Star Rating impact, or care gap closure strategies.\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],{"name":14,"slug":15,"type":16},"Healthcare","healthcare","tag",{"name":18,"slug":19,"type":16},"Audit","audit",{"name":21,"slug":22,"type":16},"Regulatory Compliance","regulatory-compliance",4,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fhcls-agent-skills","2026-07-12T08:38:35.547597",null,0,[29,30,31,32,33,34,35,36,37,38,39,40,41],"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":24,"stars":23,"forks":27,"topics":43,"description":44},[29,30,31,32,33,34,35,36,37,38,39,40,41],"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\u002Fhedis-measure-specification","---\nname: hedis-measure-specification\ndescription: >\n  Reasoning skill for HEDIS measure specification, enrollment logic, exclusion evaluation,\n  NCQA audit requirements, and care gap prioritization. Use when the user asks about HEDIS\n  measure definitions, denominator\u002Fnumerator\u002Fexclusion logic, continuous enrollment rules,\n  Star Rating impact, or care gap closure strategies.\nusage: Use when interpreting HEDIS specifications, evaluating enrollment\u002Fexclusion logic, or prioritizing care gaps.\nversion: 1.0.0\ntags: [skill, category:reasoning, hedis, quality-measures, hcls]\ntriggers:\n  - HEDIS measure\n  - quality measure\n  - denominator\n  - numerator\n  - exclusion\n  - NCQA audit\n  - continuous enrollment\n  - care gap\n  - Star Rating\n  - measure specification\n  - CDC measure\n  - BCS measure\n  - CBP measure\n---\n\n# HEDIS Measure Specification Reasoning\n\n## Overview\n\nStructured interpretation of HEDIS quality measures: denominator\u002Fnumerator logic, continuous enrollment evaluation, exclusion application, NCQA audit readiness, and Star Rating-weighted care gap prioritization. Based on NCQA HEDIS Technical Specifications (MY 2024).\n\n## Usage\n\n- Activate when interpreting HEDIS measure denominator\u002Fnumerator\u002Fexclusion logic\n- Activate when evaluating continuous enrollment rules or allowable gaps\n- Activate when prioritizing care gaps by Star Rating weight or SDOH barriers\n\n## Core Concepts\n\n## Response Format\n\nApply measure logic internally. Present the final specification, rate interpretation, or gap prioritization with justification. Do not narrate enrollment evaluation steps or exclusion logic walkthrough.\n\n## 1. HEDIS Measure Structure\n\nEvery HEDIS measure follows:\n\n```\nEligible Population (Denominator)\n  → minus Exclusions\n  → equals Eligible Denominator\n  → Numerator (members who met the quality criteria)\n  → Rate = Numerator \u002F Eligible Denominator\n```\n\n| Component | Definition | Example (CDC — Diabetes HbA1c) |\n|-----------|-----------|-------------------------------|\n| **Denominator** | Members eligible based on age, diagnosis, enrollment | Age 18–75, diabetes (E11.x), continuously enrolled |\n| **Exclusions** | Members removed due to clinical exceptions | Hospice, ESRD, organ transplant |\n| **Numerator** | Members who met the quality criteria | HbA1c test performed during measurement year |\n| **Rate** | Numerator ÷ (Denominator − Exclusions) | Percentage with HbA1c testing |\n\nFive measure types: **Process** (service delivered), **Outcome** (clinical result), **Structural** (system capability), **Patient experience** (CAHPS), **Utilization** (resource consumption).\n\n## 2. Continuous Enrollment Rules\n\n| Rule | Definition |\n|------|-----------|\n| Measurement year | January 1 – December 31 of reporting year |\n| Anchor date | Date member must be enrolled through (usually Dec 31) |\n| Allowable gap | ≤45 days total gap permitted |\n| Gap counting | Calendar days without coverage; multiple gaps summed |\n| Enrollment source | Medical and\u002For pharmacy benefit, measure-dependent |\n\n### Enrollment Evaluation Decision Tree\n\n```\nIs the member enrolled on the anchor date?\n├── NO → Exclude from denominator\n└── YES\n    ├── Total gap days during measurement year?\n    │   ├── ≤45 days → Continuously enrolled\n    │   └── >45 days → Exclude from denominator\n    └── Measure requires pharmacy benefit?\n        ├── YES → Verify pharmacy enrollment separately\n        └── NO → Medical enrollment sufficient\n```\n\n## 3. Exclusion Logic\n\n| Category | Applies To | Condition |\n|----------|-----------|-----------|\n| Hospice | All measures | Hospice benefit or encounter |\n| Deceased | All measures | Death during measurement year |\n| ESRD | Diabetes, kidney | N18.6, dialysis codes |\n| Organ transplant | Diabetes, kidney | Z94.x |\n| Pregnancy | BP, diabetes | O00-O9A |\n| Frailty + advanced illness | Age 66+, multiple | BOTH conditions required |\n\n**Evaluation rules:**\n1. Apply exclusions AFTER building the full denominator\n2. Check the full measurement year for exclusion events\n3. Frailty + advanced illness is compound — both must be present\n4. Hospice overrides all other logic\n5. Document which optional exclusions are applied\n\n## 4. NCQA Audit Requirements\n\n| Source | Priority | Use For |\n|--------|----------|---------|\n| Administrative claims | Primary | Denominator, exclusions, process numerators |\n| Electronic clinical data (ECDS) | Primary (ECDS measures) | Lab results, vitals |\n| Supplemental data | Secondary | Fills claims gaps (HIE lab results) |\n| Medical record review | Tertiary | Validation, hybrid measures |\n\n### Common Audit Findings\n\n| Finding | Severity | Remediation |\n|---------|----------|-------------|\n| Supplemental data without source verification | High | Implement source validation |\n| Enrollment gap calculation error | High | Revalidate against NCQA specs |\n| Incorrect age calculation | Medium | Use age as of anchor date |\n| Duplicate member counting | High | Deduplicate on member ID |\n| Stale value sets | Medium | Update code sets annually |\n\n## 5. Care Gap Prioritization\n\n### Prioritization Decision Tree\n\n```\nIs the measure triple-weighted for Star Ratings?\n├── YES → High priority baseline\n│   ├── Member high-risk (Charlson ≥3 or LACE ≥10)?\n│   │   ├── YES → Critical priority — immediate outreach\n│   │   └── NO → High priority — standard outreach\n│   └── SDOH barriers (Z-codes, high ADI)?\n│       ├── YES → Assign care coordinator\n│       └── NO → Automated reminder sufficient\n└── NO → Standard priority\n    ├── >6 months remaining in measurement year?\n    │   ├── YES → Schedule in next outreach batch\n    │   └── NO → Escalate if feasible\n    └── Process measure (screening\u002Ftest)?\n        ├── YES → High closure probability — include\n        └── NO → Outcome measure — coordinate with PCP\n```\n\n### Rate Interpretation\n\n| Rate Range | Star Level | Action |\n|------------|-----------|--------|\n| ≥90th percentile | 5-star | Maintain current programs |\n| 75th–89th | 4-star | Targeted improvement |\n| 50th–74th | 3-star | Systematic outreach needed |\n| 25th–49th | 2-star | Intensive intervention, root cause analysis |\n| \u003C25th | 1-star | Urgent remediation, leadership escalation |\n\n## Common Mistakes\n\n- **Wrong:** Calculating age as of data extraction date → **Right:** Use measure-specific anchor date (typically Dec 31)\n- **Wrong:** Applying exclusions before building the full denominator → **Right:** Build complete eligible population first, then subtract\n- **Wrong:** Excluding members with ≤45-day enrollment gaps → **Right:** HEDIS permits ≤45-day allowable gap\n- **Wrong:** Mixing process and outcome sub-measures (e.g., HbA1c testing vs HbA1c \u003C8%) → **Right:** Treat as separate rates\n- **Wrong:** Using prior-year value sets without updating → **Right:** Update ICD-10\u002FCPT\u002FHCPCS annually\n- **Wrong:** Counting members multiple times across enrollment segments → **Right:** Deduplicate on member ID\n- **Wrong:** Submitting supplemental data without source documentation → **Right:** Validate with date, value, provider before submission\n- **Wrong:** Treating all measures with equal priority → **Right:** Prioritize triple-weighted Star Rating measures (3× impact)\n\n## When to Escalate\n\n- Exclusion logic produces unexpected denominator drops (>10%)\n- Before submitting quality data affecting reimbursement or accreditation\n- Supplemental data sources change rates by >5 percentage points\n",{"data":48,"body":71},{"name":4,"description":6,"usage":49,"version":50,"tags":51,"triggers":57},"Use when interpreting HEDIS specifications, evaluating enrollment\u002Fexclusion logic, or prioritizing care gaps.","1.0.0",[52,53,54,55,56],"skill","category:reasoning","hedis","quality-measures","hcls",[58,59,60,61,62,63,64,65,66,67,68,69,70],"HEDIS measure","quality measure","denominator","numerator","exclusion","NCQA audit","continuous enrollment","care gap","Star Rating","measure specification","CDC measure","BCS measure","CBP measure",{"type":72,"children":73},"root",[74,83,90,96,102,122,128,134,139,145,150,163,281,321,327,413,420,429,435,568,576,605,611,710,716,830,836,842,851,857,974,980,1105,1111],{"type":75,"tag":76,"props":77,"children":79},"element","h1",{"id":78},"hedis-measure-specification-reasoning",[80],{"type":81,"value":82},"text","HEDIS Measure Specification Reasoning",{"type":75,"tag":84,"props":85,"children":87},"h2",{"id":86},"overview",[88],{"type":81,"value":89},"Overview",{"type":75,"tag":91,"props":92,"children":93},"p",{},[94],{"type":81,"value":95},"Structured interpretation of HEDIS quality measures: denominator\u002Fnumerator logic, continuous enrollment evaluation, exclusion application, NCQA audit readiness, and Star Rating-weighted care gap prioritization. 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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},[1171,1174,1175],{"name":1172,"slug":1173,"type":16},"Clinical Trials","clinical-trials",{"name":1145,"slug":38,"type":16},{"name":21,"slug":22,"type":16},"2026-07-12T08:37:33.35594",{"slug":1178,"name":1178,"fn":1179,"description":1180,"org":1181,"tags":1182,"stars":23,"repoUrl":24,"updatedAt":1191},"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},[1183,1184,1187,1188],{"name":1158,"slug":1159,"type":16},{"name":1185,"slug":1186,"type":16},"Data Analysis","data-analysis",{"name":1145,"slug":38,"type":16},{"name":1189,"slug":1190,"type":16},"RNA-seq","rna-seq","2026-07-12T08:38:05.443454",{"slug":1193,"name":1193,"fn":1194,"description":1195,"org":1196,"tags":1197,"stars":23,"repoUrl":24,"updatedAt":1204},"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},[1198,1199,1202,1203],{"name":1158,"slug":1159,"type":16},{"name":1200,"slug":1201,"type":16},"Chemistry","chemistry",{"name":1185,"slug":1186,"type":16},{"name":1162,"slug":1163,"type":16},"2026-07-12T08:37:28.334619",{"slug":1206,"name":1206,"fn":1207,"description":1208,"org":1209,"tags":1210,"stars":23,"repoUrl":24,"updatedAt":1217},"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},[1211,1212,1213,1216],{"name":1185,"slug":1186,"type":16},{"name":14,"slug":15,"type":16},{"name":1214,"slug":1215,"type":16},"Insurance","insurance",{"name":1145,"slug":38,"type":16},"2026-07-12T08:37:34.815088",{"slug":1219,"name":1219,"fn":1220,"description":1221,"org":1222,"tags":1223,"stars":23,"repoUrl":24,"updatedAt":1227},"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},[1224,1225,1226],{"name":14,"slug":15,"type":16},{"name":1214,"slug":1215,"type":16},{"name":21,"slug":22,"type":16},"2026-07-12T08:38:28.210856",40,{"items":1230,"total":1406},[1231,1250,1271,1281,1294,1307,1317,1327,1348,1363,1378,1393],{"slug":1232,"name":1232,"fn":1233,"description":1234,"org":1235,"tags":1236,"stars":1247,"repoUrl":1248,"updatedAt":1249},"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},[1237,1238,1241,1244],{"name":1141,"slug":1142,"type":16},{"name":1239,"slug":1240,"type":16},"Debugging","debugging",{"name":1242,"slug":1243,"type":16},"Logs","logs",{"name":1245,"slug":1246,"type":16},"Observability","observability",9427,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":1251,"name":1252,"fn":1253,"description":1254,"org":1255,"tags":1256,"stars":1247,"repoUrl":1248,"updatedAt":1270},"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},[1257,1260,1261,1264,1267],{"name":1258,"slug":1259,"type":16},"Aurora","aurora",{"name":1141,"slug":1142,"type":16},{"name":1262,"slug":1263,"type":16},"Database","database",{"name":1265,"slug":1266,"type":16},"Serverless","serverless",{"name":1268,"slug":1269,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":1272,"name":1273,"fn":1253,"description":1254,"org":1274,"tags":1275,"stars":1247,"repoUrl":1248,"updatedAt":1280},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1276,1277,1278,1279],{"name":1141,"slug":1142,"type":16},{"name":1262,"slug":1263,"type":16},{"name":1265,"slug":1266,"type":16},{"name":1268,"slug":1269,"type":16},"2026-07-12T08:36:42.694299",{"slug":1282,"name":1283,"fn":1253,"description":1254,"org":1284,"tags":1285,"stars":1247,"repoUrl":1248,"updatedAt":1293},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1286,1287,1288,1291,1292],{"name":1141,"slug":1142,"type":16},{"name":1262,"slug":1263,"type":16},{"name":1289,"slug":1290,"type":16},"Migration","migration",{"name":1265,"slug":1266,"type":16},{"name":1268,"slug":1269,"type":16},"2026-07-12T08:36:38.584057",{"slug":1295,"name":1296,"fn":1253,"description":1254,"org":1297,"tags":1298,"stars":1247,"repoUrl":1248,"updatedAt":1306},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1299,1300,1301,1304,1305],{"name":1141,"slug":1142,"type":16},{"name":1262,"slug":1263,"type":16},{"name":1302,"slug":1303,"type":16},"PostgreSQL","postgresql",{"name":1265,"slug":1266,"type":16},{"name":1268,"slug":1269,"type":16},"2026-07-12T08:36:46.530743",{"slug":1308,"name":1309,"fn":1253,"description":1254,"org":1310,"tags":1311,"stars":1247,"repoUrl":1248,"updatedAt":1316},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1312,1313,1314,1315],{"name":1141,"slug":1142,"type":16},{"name":1262,"slug":1263,"type":16},{"name":1265,"slug":1266,"type":16},{"name":1268,"slug":1269,"type":16},"2026-07-12T08:36:48.104182",{"slug":1318,"name":1318,"fn":1253,"description":1254,"org":1319,"tags":1320,"stars":1247,"repoUrl":1248,"updatedAt":1326},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[1321,1322,1323,1324,1325],{"name":1141,"slug":1142,"type":16},{"name":1262,"slug":1263,"type":16},{"name":1289,"slug":1290,"type":16},{"name":1265,"slug":1266,"type":16},{"name":1268,"slug":1269,"type":16},"2026-07-12T08:36:36.374512",{"slug":1328,"name":1328,"fn":1329,"description":1330,"org":1331,"tags":1332,"stars":1345,"repoUrl":1346,"updatedAt":1347},"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},[1333,1336,1339,1342],{"name":1334,"slug":1335,"type":16},"Accounting","accounting",{"name":1337,"slug":1338,"type":16},"Analytics","analytics",{"name":1340,"slug":1341,"type":16},"Cost Optimization","cost-optimization",{"name":1343,"slug":1344,"type":16},"Finance","finance",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",{"slug":1349,"name":1349,"fn":1350,"description":1351,"org":1352,"tags":1353,"stars":1345,"repoUrl":1346,"updatedAt":1362},"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},[1354,1355,1356,1359],{"name":1141,"slug":1142,"type":16},{"name":1343,"slug":1344,"type":16},{"name":1357,"slug":1358,"type":16},"Management","management",{"name":1360,"slug":1361,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":1364,"name":1364,"fn":1365,"description":1366,"org":1367,"tags":1368,"stars":1345,"repoUrl":1346,"updatedAt":1377},"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},[1369,1370,1371,1374],{"name":1337,"slug":1338,"type":16},{"name":1343,"slug":1344,"type":16},{"name":1372,"slug":1373,"type":16},"Financial Statements","financial-statements",{"name":1375,"slug":1376,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":1379,"name":1379,"fn":1380,"description":1381,"org":1382,"tags":1383,"stars":1345,"repoUrl":1346,"updatedAt":1392},"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},[1384,1387,1390],{"name":1385,"slug":1386,"type":16},"Automation","automation",{"name":1388,"slug":1389,"type":16},"Documents","documents",{"name":1391,"slug":1379,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":1394,"name":1394,"fn":1395,"description":1396,"org":1397,"tags":1398,"stars":1345,"repoUrl":1346,"updatedAt":1405},"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},[1399,1400,1401,1402],{"name":1334,"slug":1335,"type":16},{"name":1185,"slug":1186,"type":16},{"name":1343,"slug":1344,"type":16},{"name":1403,"slug":1404,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",150]