[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-quarterly-kpi-calculator":3,"mdc--scykm-key":48,"related-org-aws-labs-quarterly-kpi-calculator":428,"related-repo-aws-labs-quarterly-kpi-calculator":596},{"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":43,"sourceUrl":46,"mdContent":47},"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},"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},"Finance","finance","tag",{"name":18,"slug":19,"type":16},"KPI","kpi",{"name":21,"slug":22,"type":16},"Accounting","accounting",{"name":24,"slug":25,"type":16},"Data Analysis","data-analysis",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:39:59.54971",null,1233,[32,33,34,35,36,37,38,39,40,41,42],"agent","agentic-ai","agents","authentication","bedrock","core","gateway","identity-management","memory-management","production-code","runtime",{"repoUrl":27,"stars":26,"forks":30,"topics":44,"description":45},[32,33,34,35,36,37,38,39,40,41,42],"Amazon Bedrock Agentcore accelerates AI agents into production with the scale, reliability, and security, critical to real-world deployment.","https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples\u002Ftree\u002FHEAD\u002F01-features\u002F07-centralize-and-govern-your-ai-infrastructure\u002F03-registry\u002F03-advanced\u002Fstrands-mcp-ecs-registry\u002Fmy_skills\u002Fquarterly-kpi-calculator","---\nname: quarterly-kpi-calculator\ndescription: Calculates quarterly financial KPIs from P&L data. P&L figures can be\n  provided directly by the user or fetched from the financial data MCP server.\n  Use when the user wants KPI calculations such as Gross Margin %, EBITDA Margin %,\n  Operating Expense Ratio, or Revenue Growth % QoQ. Also use for quarterly\n  performance review, P&L analysis, or interpreting financial ratios against benchmarks.\nmetadata:\n  version: \"2.0\"\n  tags: finance, kpi, analysis, mcp\nmcp_tools:\n  - get_financial_data\n  - get_kpi_benchmarks\n---\n\n# Quarterly KPI Calculator\n\nCalculates and interprets financial KPIs. P&L data is fetched from the financial\nMCP server or taken from figures the user provides.\n\n## Prerequisites\n\nAt minimum: Revenue and COGS (provided by user or fetched via get_financial_data).\nOptional: EBITDA, Operating Expenses, prior quarter Revenue for QoQ growth.\n\n## Steps\n\n### Step 1: Retrieve benchmark thresholds\n\nCall the get_kpi_benchmarks tool to get current KPI formulas and benchmark values:\n\n    get_kpi_benchmarks()\n\nStore the result — you will use the formulas and benchmarks in Steps 3 and 4.\n\n### Step 2: Get P&L data\n\n**If the user provided P&L figures directly** (Revenue, COGS, EBITDA, Operating Expenses),\nuse those values.\n\n**If the user specified only a quarter** (e.g. \"Q3 2025\") without raw figures,\ncall get_financial_data to retrieve them:\n\n    get_financial_data(period=\"Q3 2025\")\n\n**If QoQ Revenue Growth is requested** and prior quarter data is needed,\ncall get_financial_data for the prior quarter as well:\n\n    get_financial_data(period=\"Q2 2025\")\n\n### Step 3: Calculate KPIs\n\nUse python_exec to calculate the following from the P&L data (use values from Step 2):\n\n- Gross Margin %           = (Revenue - COGS) \u002F Revenue * 100\n- EBITDA Margin %          = EBITDA \u002F Revenue * 100              (if EBITDA available)\n- Operating Expense Ratio  = Operating Expenses \u002F Revenue * 100  (if OpEx available)\n- Revenue Growth % QoQ     = (Current - Prior) \u002F Prior * 100     (if prior available)\n\nRound all percentages to one decimal place.\n\nExample:\n```python\nrevenue   = 4200000\ncogs      = 1890000\nebitda    = 1260000\nopex      = 1050000\nprior_rev = 3800000\n\ngross_margin  = round((revenue - cogs) \u002F revenue * 100, 1)\nebitda_margin = round(ebitda \u002F revenue * 100, 1)\nopex_ratio    = round(opex \u002F revenue * 100, 1)\nrev_growth    = round((revenue - prior_rev) \u002F prior_rev * 100, 1)\n\nprint(f\"Gross Margin:            {gross_margin}%\")\nprint(f\"EBITDA Margin:           {ebitda_margin}%\")\nprint(f\"Operating Expense Ratio: {opex_ratio}%\")\nprint(f\"Revenue Growth QoQ:      {rev_growth}%\")\n```\n\n### Step 4: Interpret against benchmarks\n\nUsing general_benchmark values from get_kpi_benchmarks (Step 1), assign each KPI a status:\n- GREEN  : at or above general_benchmark\n- YELLOW : within 5 percentage points below general_benchmark\n- RED    : more than 5 percentage points below general_benchmark\n\n### Step 5: Present results\n\nFormat your final response as:\n1. A KPI results table: | Metric | Value | Benchmark | Status |\n2. A 2–3 sentence executive commentary on the most significant finding.\n",{"data":49,"body":56},{"name":4,"description":6,"metadata":50,"mcp_tools":53},{"version":51,"tags":52},"2.0","finance, kpi, analysis, mcp",[54,55],"get_financial_data","get_kpi_benchmarks",{"type":57,"children":58},"root",[59,67,73,80,85,91,98,103,116,121,127,138,148,157,167,176,182,187,212,217,222,368,374,379,397,403,408,422],{"type":60,"tag":61,"props":62,"children":63},"element","h1",{"id":4},[64],{"type":65,"value":66},"text","Quarterly KPI Calculator",{"type":60,"tag":68,"props":69,"children":70},"p",{},[71],{"type":65,"value":72},"Calculates and interprets financial KPIs. P&L data is fetched from the financial\nMCP server or taken from figures the user provides.",{"type":60,"tag":74,"props":75,"children":77},"h2",{"id":76},"prerequisites",[78],{"type":65,"value":79},"Prerequisites",{"type":60,"tag":68,"props":81,"children":82},{},[83],{"type":65,"value":84},"At minimum: Revenue and COGS (provided by user or fetched via get_financial_data).\nOptional: EBITDA, Operating Expenses, prior quarter Revenue for QoQ growth.",{"type":60,"tag":74,"props":86,"children":88},{"id":87},"steps",[89],{"type":65,"value":90},"Steps",{"type":60,"tag":92,"props":93,"children":95},"h3",{"id":94},"step-1-retrieve-benchmark-thresholds",[96],{"type":65,"value":97},"Step 1: Retrieve benchmark thresholds",{"type":60,"tag":68,"props":99,"children":100},{},[101],{"type":65,"value":102},"Call the get_kpi_benchmarks tool to get current KPI formulas and benchmark values:",{"type":60,"tag":104,"props":105,"children":109},"pre",{"className":106,"code":108,"language":65},[107],"language-text","get_kpi_benchmarks()\n",[110],{"type":60,"tag":111,"props":112,"children":114},"code",{"__ignoreMap":113},"",[115],{"type":65,"value":108},{"type":60,"tag":68,"props":117,"children":118},{},[119],{"type":65,"value":120},"Store the result — you will use the formulas and benchmarks in Steps 3 and 4.",{"type":60,"tag":92,"props":122,"children":124},{"id":123},"step-2-get-pl-data",[125],{"type":65,"value":126},"Step 2: Get P&L data",{"type":60,"tag":68,"props":128,"children":129},{},[130,136],{"type":60,"tag":131,"props":132,"children":133},"strong",{},[134],{"type":65,"value":135},"If the user provided P&L figures directly",{"type":65,"value":137}," (Revenue, COGS, EBITDA, Operating Expenses),\nuse those values.",{"type":60,"tag":68,"props":139,"children":140},{},[141,146],{"type":60,"tag":131,"props":142,"children":143},{},[144],{"type":65,"value":145},"If the user specified only a quarter",{"type":65,"value":147}," (e.g. \"Q3 2025\") without raw figures,\ncall get_financial_data to retrieve them:",{"type":60,"tag":104,"props":149,"children":152},{"className":150,"code":151,"language":65},[107],"get_financial_data(period=\"Q3 2025\")\n",[153],{"type":60,"tag":111,"props":154,"children":155},{"__ignoreMap":113},[156],{"type":65,"value":151},{"type":60,"tag":68,"props":158,"children":159},{},[160,165],{"type":60,"tag":131,"props":161,"children":162},{},[163],{"type":65,"value":164},"If QoQ Revenue Growth is requested",{"type":65,"value":166}," and prior quarter data is needed,\ncall get_financial_data for the prior quarter as well:",{"type":60,"tag":104,"props":168,"children":171},{"className":169,"code":170,"language":65},[107],"get_financial_data(period=\"Q2 2025\")\n",[172],{"type":60,"tag":111,"props":173,"children":174},{"__ignoreMap":113},[175],{"type":65,"value":170},{"type":60,"tag":92,"props":177,"children":179},{"id":178},"step-3-calculate-kpis",[180],{"type":65,"value":181},"Step 3: Calculate KPIs",{"type":60,"tag":68,"props":183,"children":184},{},[185],{"type":65,"value":186},"Use python_exec to calculate the following from the P&L data (use values from Step 2):",{"type":60,"tag":188,"props":189,"children":190},"ul",{},[191,197,202,207],{"type":60,"tag":192,"props":193,"children":194},"li",{},[195],{"type":65,"value":196},"Gross Margin %           = (Revenue - COGS) \u002F Revenue * 100",{"type":60,"tag":192,"props":198,"children":199},{},[200],{"type":65,"value":201},"EBITDA Margin %          = EBITDA \u002F Revenue * 100              (if EBITDA available)",{"type":60,"tag":192,"props":203,"children":204},{},[205],{"type":65,"value":206},"Operating Expense Ratio  = Operating Expenses \u002F Revenue * 100  (if OpEx available)",{"type":60,"tag":192,"props":208,"children":209},{},[210],{"type":65,"value":211},"Revenue Growth % QoQ     = (Current - Prior) \u002F Prior * 100     (if prior available)",{"type":60,"tag":68,"props":213,"children":214},{},[215],{"type":65,"value":216},"Round all percentages to one decimal place.",{"type":60,"tag":68,"props":218,"children":219},{},[220],{"type":65,"value":221},"Example:",{"type":60,"tag":104,"props":223,"children":227},{"className":224,"code":225,"language":226,"meta":113,"style":113},"language-python shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","revenue   = 4200000\ncogs      = 1890000\nebitda    = 1260000\nopex      = 1050000\nprior_rev = 3800000\n\ngross_margin  = round((revenue - cogs) \u002F revenue * 100, 1)\nebitda_margin = round(ebitda \u002F revenue * 100, 1)\nopex_ratio    = round(opex \u002F revenue * 100, 1)\nrev_growth    = round((revenue - prior_rev) \u002F prior_rev * 100, 1)\n\nprint(f\"Gross Margin:            {gross_margin}%\")\nprint(f\"EBITDA Margin:           {ebitda_margin}%\")\nprint(f\"Operating Expense Ratio: {opex_ratio}%\")\nprint(f\"Revenue Growth QoQ:      {rev_growth}%\")\n","python",[228],{"type":60,"tag":111,"props":229,"children":230},{"__ignoreMap":113},[231,242,251,260,269,278,288,297,306,315,324,332,341,350,359],{"type":60,"tag":232,"props":233,"children":236},"span",{"class":234,"line":235},"line",1,[237],{"type":60,"tag":232,"props":238,"children":239},{},[240],{"type":65,"value":241},"revenue   = 4200000\n",{"type":60,"tag":232,"props":243,"children":245},{"class":234,"line":244},2,[246],{"type":60,"tag":232,"props":247,"children":248},{},[249],{"type":65,"value":250},"cogs      = 1890000\n",{"type":60,"tag":232,"props":252,"children":254},{"class":234,"line":253},3,[255],{"type":60,"tag":232,"props":256,"children":257},{},[258],{"type":65,"value":259},"ebitda    = 1260000\n",{"type":60,"tag":232,"props":261,"children":263},{"class":234,"line":262},4,[264],{"type":60,"tag":232,"props":265,"children":266},{},[267],{"type":65,"value":268},"opex      = 1050000\n",{"type":60,"tag":232,"props":270,"children":272},{"class":234,"line":271},5,[273],{"type":60,"tag":232,"props":274,"children":275},{},[276],{"type":65,"value":277},"prior_rev = 3800000\n",{"type":60,"tag":232,"props":279,"children":281},{"class":234,"line":280},6,[282],{"type":60,"tag":232,"props":283,"children":285},{"emptyLinePlaceholder":284},true,[286],{"type":65,"value":287},"\n",{"type":60,"tag":232,"props":289,"children":291},{"class":234,"line":290},7,[292],{"type":60,"tag":232,"props":293,"children":294},{},[295],{"type":65,"value":296},"gross_margin  = round((revenue - cogs) \u002F revenue * 100, 1)\n",{"type":60,"tag":232,"props":298,"children":300},{"class":234,"line":299},8,[301],{"type":60,"tag":232,"props":302,"children":303},{},[304],{"type":65,"value":305},"ebitda_margin = round(ebitda \u002F revenue * 100, 1)\n",{"type":60,"tag":232,"props":307,"children":309},{"class":234,"line":308},9,[310],{"type":60,"tag":232,"props":311,"children":312},{},[313],{"type":65,"value":314},"opex_ratio    = round(opex \u002F revenue * 100, 1)\n",{"type":60,"tag":232,"props":316,"children":318},{"class":234,"line":317},10,[319],{"type":60,"tag":232,"props":320,"children":321},{},[322],{"type":65,"value":323},"rev_growth    = round((revenue - prior_rev) \u002F prior_rev * 100, 1)\n",{"type":60,"tag":232,"props":325,"children":327},{"class":234,"line":326},11,[328],{"type":60,"tag":232,"props":329,"children":330},{"emptyLinePlaceholder":284},[331],{"type":65,"value":287},{"type":60,"tag":232,"props":333,"children":335},{"class":234,"line":334},12,[336],{"type":60,"tag":232,"props":337,"children":338},{},[339],{"type":65,"value":340},"print(f\"Gross Margin:            {gross_margin}%\")\n",{"type":60,"tag":232,"props":342,"children":344},{"class":234,"line":343},13,[345],{"type":60,"tag":232,"props":346,"children":347},{},[348],{"type":65,"value":349},"print(f\"EBITDA Margin:           {ebitda_margin}%\")\n",{"type":60,"tag":232,"props":351,"children":353},{"class":234,"line":352},14,[354],{"type":60,"tag":232,"props":355,"children":356},{},[357],{"type":65,"value":358},"print(f\"Operating Expense Ratio: {opex_ratio}%\")\n",{"type":60,"tag":232,"props":360,"children":362},{"class":234,"line":361},15,[363],{"type":60,"tag":232,"props":364,"children":365},{},[366],{"type":65,"value":367},"print(f\"Revenue Growth QoQ:      {rev_growth}%\")\n",{"type":60,"tag":92,"props":369,"children":371},{"id":370},"step-4-interpret-against-benchmarks",[372],{"type":65,"value":373},"Step 4: Interpret against benchmarks",{"type":60,"tag":68,"props":375,"children":376},{},[377],{"type":65,"value":378},"Using general_benchmark values from get_kpi_benchmarks (Step 1), assign each KPI a status:",{"type":60,"tag":188,"props":380,"children":381},{},[382,387,392],{"type":60,"tag":192,"props":383,"children":384},{},[385],{"type":65,"value":386},"GREEN  : at or above general_benchmark",{"type":60,"tag":192,"props":388,"children":389},{},[390],{"type":65,"value":391},"YELLOW : within 5 percentage points below general_benchmark",{"type":60,"tag":192,"props":393,"children":394},{},[395],{"type":65,"value":396},"RED    : more than 5 percentage points below general_benchmark",{"type":60,"tag":92,"props":398,"children":400},{"id":399},"step-5-present-results",[401],{"type":65,"value":402},"Step 5: Present results",{"type":60,"tag":68,"props":404,"children":405},{},[406],{"type":65,"value":407},"Format your final response as:",{"type":60,"tag":409,"props":410,"children":411},"ol",{},[412,417],{"type":60,"tag":192,"props":413,"children":414},{},[415],{"type":65,"value":416},"A KPI results table: | Metric | Value | Benchmark | Status |",{"type":60,"tag":192,"props":418,"children":419},{},[420],{"type":65,"value":421},"A 2–3 sentence executive commentary on the most significant finding.",{"type":60,"tag":423,"props":424,"children":425},"style",{},[426],{"type":65,"value":427},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":429,"total":595},[430,451,472,482,495,508,518,528,543,558,573,588],{"slug":431,"name":431,"fn":432,"description":433,"org":434,"tags":435,"stars":448,"repoUrl":449,"updatedAt":450},"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},[436,439,442,445],{"name":437,"slug":438,"type":16},"AWS","aws",{"name":440,"slug":441,"type":16},"Debugging","debugging",{"name":443,"slug":444,"type":16},"Logs","logs",{"name":446,"slug":447,"type":16},"Observability","observability",9427,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":452,"name":453,"fn":454,"description":455,"org":456,"tags":457,"stars":448,"repoUrl":449,"updatedAt":471},"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},[458,461,462,465,468],{"name":459,"slug":460,"type":16},"Aurora","aurora",{"name":437,"slug":438,"type":16},{"name":463,"slug":464,"type":16},"Database","database",{"name":466,"slug":467,"type":16},"Serverless","serverless",{"name":469,"slug":470,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":473,"name":474,"fn":454,"description":455,"org":475,"tags":476,"stars":448,"repoUrl":449,"updatedAt":481},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[477,478,479,480],{"name":437,"slug":438,"type":16},{"name":463,"slug":464,"type":16},{"name":466,"slug":467,"type":16},{"name":469,"slug":470,"type":16},"2026-07-12T08:36:42.694299",{"slug":483,"name":484,"fn":454,"description":455,"org":485,"tags":486,"stars":448,"repoUrl":449,"updatedAt":494},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[487,488,489,492,493],{"name":437,"slug":438,"type":16},{"name":463,"slug":464,"type":16},{"name":490,"slug":491,"type":16},"Migration","migration",{"name":466,"slug":467,"type":16},{"name":469,"slug":470,"type":16},"2026-07-12T08:36:38.584057",{"slug":496,"name":497,"fn":454,"description":455,"org":498,"tags":499,"stars":448,"repoUrl":449,"updatedAt":507},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[500,501,502,505,506],{"name":437,"slug":438,"type":16},{"name":463,"slug":464,"type":16},{"name":503,"slug":504,"type":16},"PostgreSQL","postgresql",{"name":466,"slug":467,"type":16},{"name":469,"slug":470,"type":16},"2026-07-12T08:36:46.530743",{"slug":509,"name":510,"fn":454,"description":455,"org":511,"tags":512,"stars":448,"repoUrl":449,"updatedAt":517},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[513,514,515,516],{"name":437,"slug":438,"type":16},{"name":463,"slug":464,"type":16},{"name":466,"slug":467,"type":16},{"name":469,"slug":470,"type":16},"2026-07-12T08:36:48.104182",{"slug":519,"name":519,"fn":454,"description":455,"org":520,"tags":521,"stars":448,"repoUrl":449,"updatedAt":527},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[522,523,524,525,526],{"name":437,"slug":438,"type":16},{"name":463,"slug":464,"type":16},{"name":490,"slug":491,"type":16},{"name":466,"slug":467,"type":16},{"name":469,"slug":470,"type":16},"2026-07-12T08:36:36.374512",{"slug":529,"name":529,"fn":530,"description":531,"org":532,"tags":533,"stars":26,"repoUrl":27,"updatedAt":542},"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},[534,535,538,541],{"name":21,"slug":22,"type":16},{"name":536,"slug":537,"type":16},"Analytics","analytics",{"name":539,"slug":540,"type":16},"Cost Optimization","cost-optimization",{"name":14,"slug":15,"type":16},"2026-07-12T08:40:03.29555",{"slug":544,"name":544,"fn":545,"description":546,"org":547,"tags":548,"stars":26,"repoUrl":27,"updatedAt":557},"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},[549,550,551,554],{"name":437,"slug":438,"type":16},{"name":14,"slug":15,"type":16},{"name":552,"slug":553,"type":16},"Management","management",{"name":555,"slug":556,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":559,"name":559,"fn":560,"description":561,"org":562,"tags":563,"stars":26,"repoUrl":27,"updatedAt":572},"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},[564,565,566,569],{"name":536,"slug":537,"type":16},{"name":14,"slug":15,"type":16},{"name":567,"slug":568,"type":16},"Financial Statements","financial-statements",{"name":570,"slug":571,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":574,"name":574,"fn":575,"description":576,"org":577,"tags":578,"stars":26,"repoUrl":27,"updatedAt":587},"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},[579,582,585],{"name":580,"slug":581,"type":16},"Automation","automation",{"name":583,"slug":584,"type":16},"Documents","documents",{"name":586,"slug":574,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":4,"name":4,"fn":5,"description":6,"org":589,"tags":590,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[591,592,593,594],{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"name":14,"slug":15,"type":16},{"name":18,"slug":19,"type":16},150,{"items":597,"total":280},[598,605,612,619,625,632],{"slug":529,"name":529,"fn":530,"description":531,"org":599,"tags":600,"stars":26,"repoUrl":27,"updatedAt":542},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[601,602,603,604],{"name":21,"slug":22,"type":16},{"name":536,"slug":537,"type":16},{"name":539,"slug":540,"type":16},{"name":14,"slug":15,"type":16},{"slug":544,"name":544,"fn":545,"description":546,"org":606,"tags":607,"stars":26,"repoUrl":27,"updatedAt":557},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[608,609,610,611],{"name":437,"slug":438,"type":16},{"name":14,"slug":15,"type":16},{"name":552,"slug":553,"type":16},{"name":555,"slug":556,"type":16},{"slug":559,"name":559,"fn":560,"description":561,"org":613,"tags":614,"stars":26,"repoUrl":27,"updatedAt":572},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[615,616,617,618],{"name":536,"slug":537,"type":16},{"name":14,"slug":15,"type":16},{"name":567,"slug":568,"type":16},{"name":570,"slug":571,"type":16},{"slug":574,"name":574,"fn":575,"description":576,"org":620,"tags":621,"stars":26,"repoUrl":27,"updatedAt":587},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[622,623,624],{"name":580,"slug":581,"type":16},{"name":583,"slug":584,"type":16},{"name":586,"slug":574,"type":16},{"slug":4,"name":4,"fn":5,"description":6,"org":626,"tags":627,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[628,629,630,631],{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"name":14,"slug":15,"type":16},{"name":18,"slug":19,"type":16},{"slug":633,"name":633,"fn":634,"description":635,"org":636,"tags":637,"stars":26,"repoUrl":27,"updatedAt":644},"revenue-growth-analyst","analyze revenue growth patterns","Deep-dives into revenue growth patterns, growth rates, and growth quality. Use when the user asks specifically about revenue growth, top-line performance, sales growth, revenue acceleration or deceleration, growth trajectory, or wants to understand what is driving revenue changes. NOT for cost or margin analysis — this skill is revenue-focused only.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[638,639,640,641],{"name":536,"slug":537,"type":16},{"name":14,"slug":15,"type":16},{"name":555,"slug":556,"type":16},{"name":642,"slug":643,"type":16},"Sales","sales","2026-07-12T08:39:58.217661"]