[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-aws-labs-cost-efficiency-analyzer":3,"mdc--7ikcrv-key":48,"related-repo-aws-labs-cost-efficiency-analyzer":607,"related-org-aws-labs-cost-efficiency-analyzer":691},{"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},"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},"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},"Cost Optimization","cost-optimization",{"name":21,"slug":22,"type":16},"Accounting","accounting",{"name":24,"slug":25,"type":16},"Analytics","analytics",3176,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fagentcore-samples","2026-07-12T08:40:03.29555",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\u002Fcost-efficiency-analyzer","---\nname: cost-efficiency-analyzer\ndescription: Analyzes cost structure, cost efficiency, and expense management from P&L data.\n  Use when the user asks about costs, expenses, COGS, operating expenses, cost ratios,\n  cost control, spending efficiency, margin compression from cost side, or wants to\n  understand where money is going. Also use for \"are we spending too much\", \"cost breakdown\",\n  \"expense analysis\", or \"how efficient are our operations\". NOT for revenue or top-line analysis.\nmetadata:\n  version: \"1.0\"\n  tags: finance, cost, expenses, efficiency, operations\nmcp_tools:\n  - get_financial_data\n  - get_kpi_benchmarks\n---\n\n# Cost Efficiency Analyzer\n\nAnalyzes cost structure and operational efficiency by examining COGS,\noperating expenses, and their ratios relative to revenue over time.\n\n## Prerequisites\n\nNo inputs required unless the user asks about a specific quarter.\nDefault: analyze the most recent quarter (Q3 2025) with comparison to Q2 2025.\n\n## Steps\n\n### Step 1: Fetch cost data\n\nFetch the quarter(s) needed:\n\n    get_financial_data(period=\"Q3 2025\")\n    get_financial_data(period=\"Q2 2025\")\n\nIf the user asks for a different quarter, fetch that instead.\n\n### Step 2: Fetch benchmarks\n\n    get_kpi_benchmarks()\n\nExtract:\n- `gross_margin_pct`: formula and benchmark (40%)\n- `opex_ratio`: formula and benchmark (30%) — note: lower is better\n\n### Step 3: Compute cost metrics\n\nUse python_exec to calculate cost efficiency metrics:\n\n```python\n# Q3 2025 data\nr3  = 4200000; cogs3 = 1890000; opex3 = 1050000; ebitda3 = 1260000\n\n# Q2 2025 data (for comparison)\nr2  = 3800000; cogs2 = 1710000; opex2 = 980000;  ebitda2 = 1110000\n\ndef cost_metrics(revenue, cogs, opex, ebitda, label):\n    gross_profit  = revenue - cogs\n    gross_margin  = round(gross_profit \u002F revenue * 100, 1)\n    cogs_pct      = round(cogs \u002F revenue * 100, 1)\n    opex_pct      = round(opex \u002F revenue * 100, 1)\n    total_cost    = cogs + opex\n    total_cost_pct = round(total_cost \u002F revenue * 100, 1)\n    ebitda_margin = round(ebitda \u002F revenue * 100, 1)\n    cost_per_rev  = round(total_cost \u002F revenue, 4)   # $ of cost per $ of revenue\n\n    print(f\"\\n{label}:\")\n    print(f\"  COGS:                ${cogs:,}  ({cogs_pct}% of revenue)\")\n    print(f\"  Operating Expenses:  ${opex:,}  ({opex_pct}% of revenue)\")\n    print(f\"  Total Cost:          ${total_cost:,}  ({total_cost_pct}% of revenue)\")\n    print(f\"  Gross Margin:        {gross_margin}%  (benchmark: 40%)\")\n    print(f\"  EBITDA Margin:       {ebitda_margin}%  (benchmark: 15%)\")\n    print(f\"  Cost per $1 revenue: ${cost_per_rev:.4f}\")\n    return {\"gross_margin\": gross_margin, \"opex_pct\": opex_pct, \"cogs_pct\": cogs_pct,\n            \"total_cost_pct\": total_cost_pct}\n\nm3 = cost_metrics(r3, cogs3, opex3, ebitda3, \"Q3 2025\")\nm2 = cost_metrics(r2, cogs2, opex2, ebitda2, \"Q2 2025\")\n\n# QoQ cost efficiency change\nprint(f\"\\nQoQ Cost Efficiency Change (Q2 → Q3):\")\nprint(f\"  COGS ratio:  {m2['cogs_pct']}% → {m3['cogs_pct']}%  ({m3['cogs_pct']-m2['cogs_pct']:+.1f}pp)\")\nprint(f\"  OpEx ratio:  {m2['opex_pct']}% → {m3['opex_pct']}%  ({m3['opex_pct']-m2['opex_pct']:+.1f}pp)\")\nprint(f\"  Total cost%: {m2['total_cost_pct']}% → {m3['total_cost_pct']}%  ({m3['total_cost_pct']-m2['total_cost_pct']:+.1f}pp)\")\n```\n\n### Step 4: Benchmark assessment\n\nFor each cost ratio, assign status vs benchmark:\n- Gross Margin:  GREEN ≥ 40%, YELLOW 35–40%, RED \u003C 35%\n- OpEx Ratio:    GREEN ≤ 30%, YELLOW 30–35%, RED > 35%  (lower is better)\n\n### Step 5: Present results\n\nFormat your response as:\n\n1. **Cost Structure Table** — current quarter\n\n   | Cost Item | Amount | % of Revenue | Benchmark | Status |\n   |-----------|--------|-------------|-----------|--------|\n\n2. **QoQ Cost Efficiency** — did cost ratios improve or worsen vs prior quarter?\n\n3. **Cost Efficiency Verdict** — one paragraph: is cost management healthy,\n   where is the risk, and what should leadership watch?\n",{"data":49,"body":56},{"name":4,"description":6,"metadata":50,"mcp_tools":53},{"version":51,"tags":52},"1.0","finance, cost, expenses, efficiency, operations",[54,55],"get_financial_data","get_kpi_benchmarks",{"type":57,"children":58},"root",[59,67,73,80,85,91,98,103,116,121,127,136,141,168,174,179,493,499,504,517,523,528,601],{"type":60,"tag":61,"props":62,"children":63},"element","h1",{"id":4},[64],{"type":65,"value":66},"text","Cost Efficiency Analyzer",{"type":60,"tag":68,"props":69,"children":70},"p",{},[71],{"type":65,"value":72},"Analyzes cost structure and operational efficiency by examining COGS,\noperating expenses, and their ratios relative to revenue over time.",{"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},"No inputs required unless the user asks about a specific quarter.\nDefault: analyze the most recent quarter (Q3 2025) with comparison to Q2 2025.",{"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-fetch-cost-data",[96],{"type":65,"value":97},"Step 1: Fetch cost data",{"type":60,"tag":68,"props":99,"children":100},{},[101],{"type":65,"value":102},"Fetch the quarter(s) needed:",{"type":60,"tag":104,"props":105,"children":109},"pre",{"className":106,"code":108,"language":65},[107],"language-text","get_financial_data(period=\"Q3 2025\")\nget_financial_data(period=\"Q2 2025\")\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},"If the user asks for a different quarter, fetch that instead.",{"type":60,"tag":92,"props":122,"children":124},{"id":123},"step-2-fetch-benchmarks",[125],{"type":65,"value":126},"Step 2: Fetch benchmarks",{"type":60,"tag":104,"props":128,"children":131},{"className":129,"code":130,"language":65},[107],"get_kpi_benchmarks()\n",[132],{"type":60,"tag":111,"props":133,"children":134},{"__ignoreMap":113},[135],{"type":65,"value":130},{"type":60,"tag":68,"props":137,"children":138},{},[139],{"type":65,"value":140},"Extract:",{"type":60,"tag":142,"props":143,"children":144},"ul",{},[145,157],{"type":60,"tag":146,"props":147,"children":148},"li",{},[149,155],{"type":60,"tag":111,"props":150,"children":152},{"className":151},[],[153],{"type":65,"value":154},"gross_margin_pct",{"type":65,"value":156},": formula and benchmark (40%)",{"type":60,"tag":146,"props":158,"children":159},{},[160,166],{"type":60,"tag":111,"props":161,"children":163},{"className":162},[],[164],{"type":65,"value":165},"opex_ratio",{"type":65,"value":167},": formula and benchmark (30%) — note: lower is better",{"type":60,"tag":92,"props":169,"children":171},{"id":170},"step-3-compute-cost-metrics",[172],{"type":65,"value":173},"Step 3: Compute cost metrics",{"type":60,"tag":68,"props":175,"children":176},{},[177],{"type":65,"value":178},"Use python_exec to calculate cost efficiency metrics:",{"type":60,"tag":104,"props":180,"children":184},{"className":181,"code":182,"language":183,"meta":113,"style":113},"language-python shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# Q3 2025 data\nr3  = 4200000; cogs3 = 1890000; opex3 = 1050000; ebitda3 = 1260000\n\n# Q2 2025 data (for comparison)\nr2  = 3800000; cogs2 = 1710000; opex2 = 980000;  ebitda2 = 1110000\n\ndef cost_metrics(revenue, cogs, opex, ebitda, label):\n    gross_profit  = revenue - cogs\n    gross_margin  = round(gross_profit \u002F revenue * 100, 1)\n    cogs_pct      = round(cogs \u002F revenue * 100, 1)\n    opex_pct      = round(opex \u002F revenue * 100, 1)\n    total_cost    = cogs + opex\n    total_cost_pct = round(total_cost \u002F revenue * 100, 1)\n    ebitda_margin = round(ebitda \u002F revenue * 100, 1)\n    cost_per_rev  = round(total_cost \u002F revenue, 4)   # $ of cost per $ of revenue\n\n    print(f\"\\n{label}:\")\n    print(f\"  COGS:                ${cogs:,}  ({cogs_pct}% of revenue)\")\n    print(f\"  Operating Expenses:  ${opex:,}  ({opex_pct}% of revenue)\")\n    print(f\"  Total Cost:          ${total_cost:,}  ({total_cost_pct}% of revenue)\")\n    print(f\"  Gross Margin:        {gross_margin}%  (benchmark: 40%)\")\n    print(f\"  EBITDA Margin:       {ebitda_margin}%  (benchmark: 15%)\")\n    print(f\"  Cost per $1 revenue: ${cost_per_rev:.4f}\")\n    return {\"gross_margin\": gross_margin, \"opex_pct\": opex_pct, \"cogs_pct\": cogs_pct,\n            \"total_cost_pct\": total_cost_pct}\n\nm3 = cost_metrics(r3, cogs3, opex3, ebitda3, \"Q3 2025\")\nm2 = cost_metrics(r2, cogs2, opex2, ebitda2, \"Q2 2025\")\n\n# QoQ cost efficiency change\nprint(f\"\\nQoQ Cost Efficiency Change (Q2 → Q3):\")\nprint(f\"  COGS ratio:  {m2['cogs_pct']}% → {m3['cogs_pct']}%  ({m3['cogs_pct']-m2['cogs_pct']:+.1f}pp)\")\nprint(f\"  OpEx ratio:  {m2['opex_pct']}% → {m3['opex_pct']}%  ({m3['opex_pct']-m2['opex_pct']:+.1f}pp)\")\nprint(f\"  Total cost%: {m2['total_cost_pct']}% → {m3['total_cost_pct']}%  ({m3['total_cost_pct']-m2['total_cost_pct']:+.1f}pp)\")\n","python",[185],{"type":60,"tag":111,"props":186,"children":187},{"__ignoreMap":113},[188,199,208,218,227,236,244,253,262,271,280,289,298,307,316,325,333,342,351,360,369,378,387,396,405,414,422,431,440,448,457,466,475,484],{"type":60,"tag":189,"props":190,"children":193},"span",{"class":191,"line":192},"line",1,[194],{"type":60,"tag":189,"props":195,"children":196},{},[197],{"type":65,"value":198},"# Q3 2025 data\n",{"type":60,"tag":189,"props":200,"children":202},{"class":191,"line":201},2,[203],{"type":60,"tag":189,"props":204,"children":205},{},[206],{"type":65,"value":207},"r3  = 4200000; cogs3 = 1890000; opex3 = 1050000; ebitda3 = 1260000\n",{"type":60,"tag":189,"props":209,"children":211},{"class":191,"line":210},3,[212],{"type":60,"tag":189,"props":213,"children":215},{"emptyLinePlaceholder":214},true,[216],{"type":65,"value":217},"\n",{"type":60,"tag":189,"props":219,"children":221},{"class":191,"line":220},4,[222],{"type":60,"tag":189,"props":223,"children":224},{},[225],{"type":65,"value":226},"# Q2 2025 data (for comparison)\n",{"type":60,"tag":189,"props":228,"children":230},{"class":191,"line":229},5,[231],{"type":60,"tag":189,"props":232,"children":233},{},[234],{"type":65,"value":235},"r2  = 3800000; cogs2 = 1710000; opex2 = 980000;  ebitda2 = 1110000\n",{"type":60,"tag":189,"props":237,"children":239},{"class":191,"line":238},6,[240],{"type":60,"tag":189,"props":241,"children":242},{"emptyLinePlaceholder":214},[243],{"type":65,"value":217},{"type":60,"tag":189,"props":245,"children":247},{"class":191,"line":246},7,[248],{"type":60,"tag":189,"props":249,"children":250},{},[251],{"type":65,"value":252},"def cost_metrics(revenue, cogs, opex, ebitda, label):\n",{"type":60,"tag":189,"props":254,"children":256},{"class":191,"line":255},8,[257],{"type":60,"tag":189,"props":258,"children":259},{},[260],{"type":65,"value":261},"    gross_profit  = revenue - cogs\n",{"type":60,"tag":189,"props":263,"children":265},{"class":191,"line":264},9,[266],{"type":60,"tag":189,"props":267,"children":268},{},[269],{"type":65,"value":270},"    gross_margin  = round(gross_profit \u002F revenue * 100, 1)\n",{"type":60,"tag":189,"props":272,"children":274},{"class":191,"line":273},10,[275],{"type":60,"tag":189,"props":276,"children":277},{},[278],{"type":65,"value":279},"    cogs_pct      = round(cogs \u002F revenue * 100, 1)\n",{"type":60,"tag":189,"props":281,"children":283},{"class":191,"line":282},11,[284],{"type":60,"tag":189,"props":285,"children":286},{},[287],{"type":65,"value":288},"    opex_pct      = round(opex \u002F revenue * 100, 1)\n",{"type":60,"tag":189,"props":290,"children":292},{"class":191,"line":291},12,[293],{"type":60,"tag":189,"props":294,"children":295},{},[296],{"type":65,"value":297},"    total_cost    = cogs + opex\n",{"type":60,"tag":189,"props":299,"children":301},{"class":191,"line":300},13,[302],{"type":60,"tag":189,"props":303,"children":304},{},[305],{"type":65,"value":306},"    total_cost_pct = round(total_cost \u002F revenue * 100, 1)\n",{"type":60,"tag":189,"props":308,"children":310},{"class":191,"line":309},14,[311],{"type":60,"tag":189,"props":312,"children":313},{},[314],{"type":65,"value":315},"    ebitda_margin = round(ebitda \u002F revenue * 100, 1)\n",{"type":60,"tag":189,"props":317,"children":319},{"class":191,"line":318},15,[320],{"type":60,"tag":189,"props":321,"children":322},{},[323],{"type":65,"value":324},"    cost_per_rev  = round(total_cost \u002F revenue, 4)   # $ of cost per $ of revenue\n",{"type":60,"tag":189,"props":326,"children":328},{"class":191,"line":327},16,[329],{"type":60,"tag":189,"props":330,"children":331},{"emptyLinePlaceholder":214},[332],{"type":65,"value":217},{"type":60,"tag":189,"props":334,"children":336},{"class":191,"line":335},17,[337],{"type":60,"tag":189,"props":338,"children":339},{},[340],{"type":65,"value":341},"    print(f\"\\n{label}:\")\n",{"type":60,"tag":189,"props":343,"children":345},{"class":191,"line":344},18,[346],{"type":60,"tag":189,"props":347,"children":348},{},[349],{"type":65,"value":350},"    print(f\"  COGS:                ${cogs:,}  ({cogs_pct}% of revenue)\")\n",{"type":60,"tag":189,"props":352,"children":354},{"class":191,"line":353},19,[355],{"type":60,"tag":189,"props":356,"children":357},{},[358],{"type":65,"value":359},"    print(f\"  Operating Expenses:  ${opex:,}  ({opex_pct}% of revenue)\")\n",{"type":60,"tag":189,"props":361,"children":363},{"class":191,"line":362},20,[364],{"type":60,"tag":189,"props":365,"children":366},{},[367],{"type":65,"value":368},"    print(f\"  Total Cost:          ${total_cost:,}  ({total_cost_pct}% of revenue)\")\n",{"type":60,"tag":189,"props":370,"children":372},{"class":191,"line":371},21,[373],{"type":60,"tag":189,"props":374,"children":375},{},[376],{"type":65,"value":377},"    print(f\"  Gross Margin:        {gross_margin}%  (benchmark: 40%)\")\n",{"type":60,"tag":189,"props":379,"children":381},{"class":191,"line":380},22,[382],{"type":60,"tag":189,"props":383,"children":384},{},[385],{"type":65,"value":386},"    print(f\"  EBITDA Margin:       {ebitda_margin}%  (benchmark: 15%)\")\n",{"type":60,"tag":189,"props":388,"children":390},{"class":191,"line":389},23,[391],{"type":60,"tag":189,"props":392,"children":393},{},[394],{"type":65,"value":395},"    print(f\"  Cost per $1 revenue: ${cost_per_rev:.4f}\")\n",{"type":60,"tag":189,"props":397,"children":399},{"class":191,"line":398},24,[400],{"type":60,"tag":189,"props":401,"children":402},{},[403],{"type":65,"value":404},"    return {\"gross_margin\": gross_margin, \"opex_pct\": opex_pct, \"cogs_pct\": cogs_pct,\n",{"type":60,"tag":189,"props":406,"children":408},{"class":191,"line":407},25,[409],{"type":60,"tag":189,"props":410,"children":411},{},[412],{"type":65,"value":413},"            \"total_cost_pct\": total_cost_pct}\n",{"type":60,"tag":189,"props":415,"children":417},{"class":191,"line":416},26,[418],{"type":60,"tag":189,"props":419,"children":420},{"emptyLinePlaceholder":214},[421],{"type":65,"value":217},{"type":60,"tag":189,"props":423,"children":425},{"class":191,"line":424},27,[426],{"type":60,"tag":189,"props":427,"children":428},{},[429],{"type":65,"value":430},"m3 = cost_metrics(r3, cogs3, opex3, ebitda3, \"Q3 2025\")\n",{"type":60,"tag":189,"props":432,"children":434},{"class":191,"line":433},28,[435],{"type":60,"tag":189,"props":436,"children":437},{},[438],{"type":65,"value":439},"m2 = cost_metrics(r2, cogs2, opex2, ebitda2, \"Q2 2025\")\n",{"type":60,"tag":189,"props":441,"children":443},{"class":191,"line":442},29,[444],{"type":60,"tag":189,"props":445,"children":446},{"emptyLinePlaceholder":214},[447],{"type":65,"value":217},{"type":60,"tag":189,"props":449,"children":451},{"class":191,"line":450},30,[452],{"type":60,"tag":189,"props":453,"children":454},{},[455],{"type":65,"value":456},"# QoQ cost efficiency change\n",{"type":60,"tag":189,"props":458,"children":460},{"class":191,"line":459},31,[461],{"type":60,"tag":189,"props":462,"children":463},{},[464],{"type":65,"value":465},"print(f\"\\nQoQ Cost Efficiency Change (Q2 → Q3):\")\n",{"type":60,"tag":189,"props":467,"children":469},{"class":191,"line":468},32,[470],{"type":60,"tag":189,"props":471,"children":472},{},[473],{"type":65,"value":474},"print(f\"  COGS ratio:  {m2['cogs_pct']}% → {m3['cogs_pct']}%  ({m3['cogs_pct']-m2['cogs_pct']:+.1f}pp)\")\n",{"type":60,"tag":189,"props":476,"children":478},{"class":191,"line":477},33,[479],{"type":60,"tag":189,"props":480,"children":481},{},[482],{"type":65,"value":483},"print(f\"  OpEx ratio:  {m2['opex_pct']}% → {m3['opex_pct']}%  ({m3['opex_pct']-m2['opex_pct']:+.1f}pp)\")\n",{"type":60,"tag":189,"props":485,"children":487},{"class":191,"line":486},34,[488],{"type":60,"tag":189,"props":489,"children":490},{},[491],{"type":65,"value":492},"print(f\"  Total cost%: {m2['total_cost_pct']}% → {m3['total_cost_pct']}%  ({m3['total_cost_pct']-m2['total_cost_pct']:+.1f}pp)\")\n",{"type":60,"tag":92,"props":494,"children":496},{"id":495},"step-4-benchmark-assessment",[497],{"type":65,"value":498},"Step 4: Benchmark assessment",{"type":60,"tag":68,"props":500,"children":501},{},[502],{"type":65,"value":503},"For each cost ratio, assign status vs benchmark:",{"type":60,"tag":142,"props":505,"children":506},{},[507,512],{"type":60,"tag":146,"props":508,"children":509},{},[510],{"type":65,"value":511},"Gross Margin:  GREEN ≥ 40%, YELLOW 35–40%, RED \u003C 35%",{"type":60,"tag":146,"props":513,"children":514},{},[515],{"type":65,"value":516},"OpEx Ratio:    GREEN ≤ 30%, YELLOW 30–35%, RED > 35%  (lower is better)",{"type":60,"tag":92,"props":518,"children":520},{"id":519},"step-5-present-results",[521],{"type":65,"value":522},"Step 5: Present results",{"type":60,"tag":68,"props":524,"children":525},{},[526],{"type":65,"value":527},"Format your response as:",{"type":60,"tag":529,"props":530,"children":531},"ol",{},[532,581,591],{"type":60,"tag":146,"props":533,"children":534},{},[535,541,543],{"type":60,"tag":536,"props":537,"children":538},"strong",{},[539],{"type":65,"value":540},"Cost Structure Table",{"type":65,"value":542}," — current quarter",{"type":60,"tag":544,"props":545,"children":546},"table",{},[547],{"type":60,"tag":548,"props":549,"children":550},"thead",{},[551],{"type":60,"tag":552,"props":553,"children":554},"tr",{},[555,561,566,571,576],{"type":60,"tag":556,"props":557,"children":558},"th",{},[559],{"type":65,"value":560},"Cost Item",{"type":60,"tag":556,"props":562,"children":563},{},[564],{"type":65,"value":565},"Amount",{"type":60,"tag":556,"props":567,"children":568},{},[569],{"type":65,"value":570},"% of Revenue",{"type":60,"tag":556,"props":572,"children":573},{},[574],{"type":65,"value":575},"Benchmark",{"type":60,"tag":556,"props":577,"children":578},{},[579],{"type":65,"value":580},"Status",{"type":60,"tag":146,"props":582,"children":583},{},[584,589],{"type":60,"tag":536,"props":585,"children":586},{},[587],{"type":65,"value":588},"QoQ Cost Efficiency",{"type":65,"value":590}," — did cost ratios improve or worsen vs prior quarter?",{"type":60,"tag":146,"props":592,"children":593},{},[594,599],{"type":60,"tag":536,"props":595,"children":596},{},[597],{"type":65,"value":598},"Cost Efficiency Verdict",{"type":65,"value":600}," — one paragraph: is cost management healthy,\nwhere is the risk, and what should leadership watch?",{"type":60,"tag":602,"props":603,"children":604},"style",{},[605],{"type":65,"value":606},"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":608,"total":238},[609,616,633,648,663,678],{"slug":4,"name":4,"fn":5,"description":6,"org":610,"tags":611,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[612,613,614,615],{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":617,"name":617,"fn":618,"description":619,"org":620,"tags":621,"stars":26,"repoUrl":27,"updatedAt":632},"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},[622,625,626,629],{"name":623,"slug":624,"type":16},"AWS","aws",{"name":14,"slug":15,"type":16},{"name":627,"slug":628,"type":16},"Management","management",{"name":630,"slug":631,"type":16},"Reporting","reporting","2026-07-12T08:40:02.066471",{"slug":634,"name":634,"fn":635,"description":636,"org":637,"tags":638,"stars":26,"repoUrl":27,"updatedAt":647},"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},[639,640,641,644],{"name":24,"slug":25,"type":16},{"name":14,"slug":15,"type":16},{"name":642,"slug":643,"type":16},"Financial Statements","financial-statements",{"name":645,"slug":646,"type":16},"Variance Analysis","variance-analysis","2026-07-12T08:40:00.79141",{"slug":649,"name":649,"fn":650,"description":651,"org":652,"tags":653,"stars":26,"repoUrl":27,"updatedAt":662},"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},[654,657,660],{"name":655,"slug":656,"type":16},"Automation","automation",{"name":658,"slug":659,"type":16},"Documents","documents",{"name":661,"slug":649,"type":16},"PDF","2026-07-12T08:41:44.135656",{"slug":664,"name":664,"fn":665,"description":666,"org":667,"tags":668,"stars":26,"repoUrl":27,"updatedAt":677},"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},[669,670,673,674],{"name":21,"slug":22,"type":16},{"name":671,"slug":672,"type":16},"Data Analysis","data-analysis",{"name":14,"slug":15,"type":16},{"name":675,"slug":676,"type":16},"KPI","kpi","2026-07-12T08:39:59.54971",{"slug":679,"name":679,"fn":680,"description":681,"org":682,"tags":683,"stars":26,"repoUrl":27,"updatedAt":690},"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},[684,685,686,687],{"name":24,"slug":25,"type":16},{"name":14,"slug":15,"type":16},{"name":630,"slug":631,"type":16},{"name":688,"slug":689,"type":16},"Sales","sales","2026-07-12T08:39:58.217661",{"items":692,"total":823},[693,712,733,743,756,769,779,789,796,803,810,816],{"slug":694,"name":694,"fn":695,"description":696,"org":697,"tags":698,"stars":709,"repoUrl":710,"updatedAt":711},"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},[699,700,703,706],{"name":623,"slug":624,"type":16},{"name":701,"slug":702,"type":16},"Debugging","debugging",{"name":704,"slug":705,"type":16},"Logs","logs",{"name":707,"slug":708,"type":16},"Observability","observability",9427,"https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fmcp","2026-07-12T08:37:22.601527",{"slug":713,"name":714,"fn":715,"description":716,"org":717,"tags":718,"stars":709,"repoUrl":710,"updatedAt":732},"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},[719,722,723,726,729],{"name":720,"slug":721,"type":16},"Aurora","aurora",{"name":623,"slug":624,"type":16},{"name":724,"slug":725,"type":16},"Database","database",{"name":727,"slug":728,"type":16},"Serverless","serverless",{"name":730,"slug":731,"type":16},"SQL","sql","2026-07-12T08:36:45.053393",{"slug":734,"name":735,"fn":715,"description":716,"org":736,"tags":737,"stars":709,"repoUrl":710,"updatedAt":742},"aurora-dsql","aurora dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[738,739,740,741],{"name":623,"slug":624,"type":16},{"name":724,"slug":725,"type":16},{"name":727,"slug":728,"type":16},{"name":730,"slug":731,"type":16},"2026-07-12T08:36:42.694299",{"slug":744,"name":745,"fn":715,"description":716,"org":746,"tags":747,"stars":709,"repoUrl":710,"updatedAt":755},"aws-dsql","aws dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[748,749,750,753,754],{"name":623,"slug":624,"type":16},{"name":724,"slug":725,"type":16},{"name":751,"slug":752,"type":16},"Migration","migration",{"name":727,"slug":728,"type":16},{"name":730,"slug":731,"type":16},"2026-07-12T08:36:38.584057",{"slug":757,"name":758,"fn":715,"description":716,"org":759,"tags":760,"stars":709,"repoUrl":710,"updatedAt":768},"distributed-postgres","distributed postgres",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[761,762,763,766,767],{"name":623,"slug":624,"type":16},{"name":724,"slug":725,"type":16},{"name":764,"slug":765,"type":16},"PostgreSQL","postgresql",{"name":727,"slug":728,"type":16},{"name":730,"slug":731,"type":16},"2026-07-12T08:36:46.530743",{"slug":770,"name":771,"fn":715,"description":716,"org":772,"tags":773,"stars":709,"repoUrl":710,"updatedAt":778},"distributed-sql","distributed sql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[774,775,776,777],{"name":623,"slug":624,"type":16},{"name":724,"slug":725,"type":16},{"name":727,"slug":728,"type":16},{"name":730,"slug":731,"type":16},"2026-07-12T08:36:48.104182",{"slug":780,"name":780,"fn":715,"description":716,"org":781,"tags":782,"stars":709,"repoUrl":710,"updatedAt":788},"dsql",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[783,784,785,786,787],{"name":623,"slug":624,"type":16},{"name":724,"slug":725,"type":16},{"name":751,"slug":752,"type":16},{"name":727,"slug":728,"type":16},{"name":730,"slug":731,"type":16},"2026-07-12T08:36:36.374512",{"slug":4,"name":4,"fn":5,"description":6,"org":790,"tags":791,"stars":26,"repoUrl":27,"updatedAt":28},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[792,793,794,795],{"name":21,"slug":22,"type":16},{"name":24,"slug":25,"type":16},{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"slug":617,"name":617,"fn":618,"description":619,"org":797,"tags":798,"stars":26,"repoUrl":27,"updatedAt":632},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[799,800,801,802],{"name":623,"slug":624,"type":16},{"name":14,"slug":15,"type":16},{"name":627,"slug":628,"type":16},{"name":630,"slug":631,"type":16},{"slug":634,"name":634,"fn":635,"description":636,"org":804,"tags":805,"stars":26,"repoUrl":27,"updatedAt":647},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[806,807,808,809],{"name":24,"slug":25,"type":16},{"name":14,"slug":15,"type":16},{"name":642,"slug":643,"type":16},{"name":645,"slug":646,"type":16},{"slug":649,"name":649,"fn":650,"description":651,"org":811,"tags":812,"stars":26,"repoUrl":27,"updatedAt":662},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[813,814,815],{"name":655,"slug":656,"type":16},{"name":658,"slug":659,"type":16},{"name":661,"slug":649,"type":16},{"slug":664,"name":664,"fn":665,"description":666,"org":817,"tags":818,"stars":26,"repoUrl":27,"updatedAt":677},{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[819,820,821,822],{"name":21,"slug":22,"type":16},{"name":671,"slug":672,"type":16},{"name":14,"slug":15,"type":16},{"name":675,"slug":676,"type":16},150]