{ "schemaVersion": "1.0", "item": { "slug": "revenue-operations", "name": "Revenue Operations", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/alirezarezvani/revenue-operations", "canonicalUrl": "https://clawhub.ai/alirezarezvani/revenue-operations", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/revenue-operations", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=revenue-operations", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "SKILL.md", "assets/expected_output.json", "assets/forecast_report_template.md", "assets/gtm_dashboard_template.md", "assets/pipeline_review_template.md", "assets/sample_forecast_data.json" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/revenue-operations" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/revenue-operations", "agentPageUrl": "https://openagent3.xyz/skills/revenue-operations/agent", "manifestUrl": "https://openagent3.xyz/skills/revenue-operations/agent.json", "briefUrl": "https://openagent3.xyz/skills/revenue-operations/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Revenue Operations", "body": "Pipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams.\n\nOutput formats: All scripts support --format text (human-readable) and --format json (dashboards/integrations)." }, { "title": "Quick Start", "body": "# Analyze pipeline health and coverage\npython scripts/pipeline_analyzer.py --input assets/sample_pipeline_data.json --format text\n\n# Track forecast accuracy over multiple periods\npython scripts/forecast_accuracy_tracker.py assets/sample_forecast_data.json --format text\n\n# Calculate GTM efficiency metrics\npython scripts/gtm_efficiency_calculator.py assets/sample_gtm_data.json --format text" }, { "title": "1. Pipeline Analyzer", "body": "Analyzes sales pipeline health including coverage ratios, stage conversion rates, deal velocity, aging risks, and concentration risks.\n\nInput: JSON file with deals, quota, and stage configuration\nOutput: Coverage ratios, conversion rates, velocity metrics, aging flags, risk assessment\n\nUsage:\n\npython scripts/pipeline_analyzer.py --input pipeline.json --format text\n\nKey Metrics Calculated:\n\nPipeline Coverage Ratio -- Total pipeline value / quota target (healthy: 3-4x)\nStage Conversion Rates -- Stage-to-stage progression rates\nSales Velocity -- (Opportunities x Avg Deal Size x Win Rate) / Avg Sales Cycle\nDeal Aging -- Flags deals exceeding 2x average cycle time per stage\nConcentration Risk -- Warns when >40% of pipeline is in a single deal\nCoverage Gap Analysis -- Identifies quarters with insufficient pipeline\n\nInput Schema:\n\n{\n \"quota\": 500000,\n \"stages\": [\"Discovery\", \"Qualification\", \"Proposal\", \"Negotiation\", \"Closed Won\"],\n \"average_cycle_days\": 45,\n \"deals\": [\n {\n \"id\": \"D001\",\n \"name\": \"Acme Corp\",\n \"stage\": \"Proposal\",\n \"value\": 85000,\n \"age_days\": 32,\n \"close_date\": \"2025-03-15\",\n \"owner\": \"rep_1\"\n }\n ]\n}" }, { "title": "2. Forecast Accuracy Tracker", "body": "Tracks forecast accuracy over time using MAPE, detects systematic bias, analyzes trends, and provides category-level breakdowns.\n\nInput: JSON file with forecast periods and optional category breakdowns\nOutput: MAPE score, bias analysis, trends, category breakdown, accuracy rating\n\nUsage:\n\npython scripts/forecast_accuracy_tracker.py forecast_data.json --format text\n\nKey Metrics Calculated:\n\nMAPE -- mean(|actual - forecast| / |actual|) x 100\nForecast Bias -- Over-forecasting (positive) vs under-forecasting (negative) tendency\nWeighted Accuracy -- MAPE weighted by deal value for materiality\nPeriod Trends -- Improving, stable, or declining accuracy over time\nCategory Breakdown -- Accuracy by rep, product, segment, or any custom dimension\n\nAccuracy Ratings:\n\nRatingMAPE RangeInterpretationExcellent<10%Highly predictable, data-driven processGood10-15%Reliable forecasting with minor varianceFair15-25%Needs process improvementPoor>25%Significant forecasting methodology gaps\n\nInput Schema:\n\n{\n \"forecast_periods\": [\n {\"period\": \"2025-Q1\", \"forecast\": 480000, \"actual\": 520000},\n {\"period\": \"2025-Q2\", \"forecast\": 550000, \"actual\": 510000}\n ],\n \"category_breakdowns\": {\n \"by_rep\": [\n {\"category\": \"Rep A\", \"forecast\": 200000, \"actual\": 210000},\n {\"category\": \"Rep B\", \"forecast\": 280000, \"actual\": 310000}\n ]\n }\n}" }, { "title": "3. GTM Efficiency Calculator", "body": "Calculates core SaaS GTM efficiency metrics with industry benchmarking, ratings, and improvement recommendations.\n\nInput: JSON file with revenue, cost, and customer metrics\nOutput: Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR with ratings\n\nUsage:\n\npython scripts/gtm_efficiency_calculator.py gtm_data.json --format text\n\nKey Metrics Calculated:\n\nMetricFormulaTargetMagic NumberNet New ARR / Prior Period S&M Spend>0.75LTV:CAC(ARPA x Gross Margin / Churn Rate) / CAC>3:1CAC PaybackCAC / (ARPA x Gross Margin) months<18 monthsBurn MultipleNet Burn / Net New ARR<2xRule of 40Revenue Growth % + FCF Margin %>40%Net Dollar Retention(Begin ARR + Expansion - Contraction - Churn) / Begin ARR>110%\n\nInput Schema:\n\n{\n \"revenue\": {\n \"current_arr\": 5000000,\n \"prior_arr\": 3800000,\n \"net_new_arr\": 1200000,\n \"arpa_monthly\": 2500,\n \"revenue_growth_pct\": 31.6\n },\n \"costs\": {\n \"sales_marketing_spend\": 1800000,\n \"cac\": 18000,\n \"gross_margin_pct\": 78,\n \"total_operating_expense\": 6500000,\n \"net_burn\": 1500000,\n \"fcf_margin_pct\": 8.4\n },\n \"customers\": {\n \"beginning_arr\": 3800000,\n \"expansion_arr\": 600000,\n \"contraction_arr\": 100000,\n \"churned_arr\": 300000,\n \"annual_churn_rate_pct\": 8\n }\n}" }, { "title": "Weekly Pipeline Review", "body": "Use this workflow for your weekly pipeline inspection cadence.\n\nVerify input data: Confirm pipeline export is current and all required fields (stage, value, close_date, owner) are populated before proceeding.\n\n\nGenerate pipeline report:\npython scripts/pipeline_analyzer.py --input current_pipeline.json --format text\n\n\n\nCross-check output totals against your CRM source system to confirm data integrity.\n\n\nReview key indicators:\n\nPipeline coverage ratio (is it above 3x quota?)\nDeals aging beyond threshold (which deals need intervention?)\nConcentration risk (are we over-reliant on a few large deals?)\nStage distribution (is there a healthy funnel shape?)\n\n\n\nDocument using template: Use assets/pipeline_review_template.md\n\n\nAction items: Address aging deals, redistribute pipeline concentration, fill coverage gaps" }, { "title": "Forecast Accuracy Review", "body": "Use monthly or quarterly to evaluate and improve forecasting discipline.\n\nVerify input data: Confirm all forecast periods have corresponding actuals and no periods are missing before running.\n\n\nGenerate accuracy report:\npython scripts/forecast_accuracy_tracker.py forecast_history.json --format text\n\n\n\nCross-check actuals against closed-won records in your CRM before drawing conclusions.\n\n\nAnalyze patterns:\n\nIs MAPE trending down (improving)?\nWhich reps or segments have the highest error rates?\nIs there systematic over- or under-forecasting?\n\n\n\nDocument using template: Use assets/forecast_report_template.md\n\n\nImprovement actions: Coach high-bias reps, adjust methodology, improve data hygiene" }, { "title": "GTM Efficiency Audit", "body": "Use quarterly or during board prep to evaluate go-to-market efficiency.\n\nVerify input data: Confirm revenue, cost, and customer figures reconcile with finance records before running.\n\n\nCalculate efficiency metrics:\npython scripts/gtm_efficiency_calculator.py quarterly_data.json --format text\n\n\n\nCross-check computed ARR and spend totals against your finance system before sharing results.\n\n\nBenchmark against targets:\n\nMagic Number (>0.75)\nLTV:CAC (>3:1)\nCAC Payback (<18 months)\nRule of 40 (>40%)\n\n\n\nDocument using template: Use assets/gtm_dashboard_template.md\n\n\nStrategic decisions: Adjust spend allocation, optimize channels, improve retention" }, { "title": "Quarterly Business Review", "body": "Combine all three tools for a comprehensive QBR analysis.\n\nRun pipeline analyzer for forward-looking coverage\nRun forecast tracker for backward-looking accuracy\nRun GTM calculator for efficiency benchmarks\nCross-reference pipeline health with forecast accuracy\nAlign GTM efficiency metrics with growth targets" }, { "title": "Reference Documentation", "body": "ReferenceDescriptionRevOps Metrics GuideComplete metrics hierarchy, definitions, formulas, and interpretationPipeline Management FrameworkPipeline best practices, stage definitions, conversion benchmarksGTM Efficiency BenchmarksSaaS benchmarks by stage, industry standards, improvement strategies" }, { "title": "Templates", "body": "TemplateUse CasePipeline Review TemplateWeekly/monthly pipeline inspection documentationForecast Report TemplateForecast accuracy reporting and trend analysisGTM Dashboard TemplateGTM efficiency dashboard for leadership reviewSample Pipeline DataExample input for pipeline_analyzer.pyExpected OutputReference output from pipeline_analyzer.py" } ], "body": "Revenue Operations\n\nPipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams.\n\nOutput formats: All scripts support --format text (human-readable) and --format json (dashboards/integrations).\n\nQuick Start\n# Analyze pipeline health and coverage\npython scripts/pipeline_analyzer.py --input assets/sample_pipeline_data.json --format text\n\n# Track forecast accuracy over multiple periods\npython scripts/forecast_accuracy_tracker.py assets/sample_forecast_data.json --format text\n\n# Calculate GTM efficiency metrics\npython scripts/gtm_efficiency_calculator.py assets/sample_gtm_data.json --format text\n\nTools Overview\n1. Pipeline Analyzer\n\nAnalyzes sales pipeline health including coverage ratios, stage conversion rates, deal velocity, aging risks, and concentration risks.\n\nInput: JSON file with deals, quota, and stage configuration Output: Coverage ratios, conversion rates, velocity metrics, aging flags, risk assessment\n\nUsage:\n\npython scripts/pipeline_analyzer.py --input pipeline.json --format text\n\n\nKey Metrics Calculated:\n\nPipeline Coverage Ratio -- Total pipeline value / quota target (healthy: 3-4x)\nStage Conversion Rates -- Stage-to-stage progression rates\nSales Velocity -- (Opportunities x Avg Deal Size x Win Rate) / Avg Sales Cycle\nDeal Aging -- Flags deals exceeding 2x average cycle time per stage\nConcentration Risk -- Warns when >40% of pipeline is in a single deal\nCoverage Gap Analysis -- Identifies quarters with insufficient pipeline\n\nInput Schema:\n\n{\n \"quota\": 500000,\n \"stages\": [\"Discovery\", \"Qualification\", \"Proposal\", \"Negotiation\", \"Closed Won\"],\n \"average_cycle_days\": 45,\n \"deals\": [\n {\n \"id\": \"D001\",\n \"name\": \"Acme Corp\",\n \"stage\": \"Proposal\",\n \"value\": 85000,\n \"age_days\": 32,\n \"close_date\": \"2025-03-15\",\n \"owner\": \"rep_1\"\n }\n ]\n}\n\n2. Forecast Accuracy Tracker\n\nTracks forecast accuracy over time using MAPE, detects systematic bias, analyzes trends, and provides category-level breakdowns.\n\nInput: JSON file with forecast periods and optional category breakdowns Output: MAPE score, bias analysis, trends, category breakdown, accuracy rating\n\nUsage:\n\npython scripts/forecast_accuracy_tracker.py forecast_data.json --format text\n\n\nKey Metrics Calculated:\n\nMAPE -- mean(|actual - forecast| / |actual|) x 100\nForecast Bias -- Over-forecasting (positive) vs under-forecasting (negative) tendency\nWeighted Accuracy -- MAPE weighted by deal value for materiality\nPeriod Trends -- Improving, stable, or declining accuracy over time\nCategory Breakdown -- Accuracy by rep, product, segment, or any custom dimension\n\nAccuracy Ratings:\n\nRating\tMAPE Range\tInterpretation\nExcellent\t<10%\tHighly predictable, data-driven process\nGood\t10-15%\tReliable forecasting with minor variance\nFair\t15-25%\tNeeds process improvement\nPoor\t>25%\tSignificant forecasting methodology gaps\n\nInput Schema:\n\n{\n \"forecast_periods\": [\n {\"period\": \"2025-Q1\", \"forecast\": 480000, \"actual\": 520000},\n {\"period\": \"2025-Q2\", \"forecast\": 550000, \"actual\": 510000}\n ],\n \"category_breakdowns\": {\n \"by_rep\": [\n {\"category\": \"Rep A\", \"forecast\": 200000, \"actual\": 210000},\n {\"category\": \"Rep B\", \"forecast\": 280000, \"actual\": 310000}\n ]\n }\n}\n\n3. GTM Efficiency Calculator\n\nCalculates core SaaS GTM efficiency metrics with industry benchmarking, ratings, and improvement recommendations.\n\nInput: JSON file with revenue, cost, and customer metrics Output: Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR with ratings\n\nUsage:\n\npython scripts/gtm_efficiency_calculator.py gtm_data.json --format text\n\n\nKey Metrics Calculated:\n\nMetric\tFormula\tTarget\nMagic Number\tNet New ARR / Prior Period S&M Spend\t>0.75\nLTV:CAC\t(ARPA x Gross Margin / Churn Rate) / CAC\t>3:1\nCAC Payback\tCAC / (ARPA x Gross Margin) months\t<18 months\nBurn Multiple\tNet Burn / Net New ARR\t<2x\nRule of 40\tRevenue Growth % + FCF Margin %\t>40%\nNet Dollar Retention\t(Begin ARR + Expansion - Contraction - Churn) / Begin ARR\t>110%\n\nInput Schema:\n\n{\n \"revenue\": {\n \"current_arr\": 5000000,\n \"prior_arr\": 3800000,\n \"net_new_arr\": 1200000,\n \"arpa_monthly\": 2500,\n \"revenue_growth_pct\": 31.6\n },\n \"costs\": {\n \"sales_marketing_spend\": 1800000,\n \"cac\": 18000,\n \"gross_margin_pct\": 78,\n \"total_operating_expense\": 6500000,\n \"net_burn\": 1500000,\n \"fcf_margin_pct\": 8.4\n },\n \"customers\": {\n \"beginning_arr\": 3800000,\n \"expansion_arr\": 600000,\n \"contraction_arr\": 100000,\n \"churned_arr\": 300000,\n \"annual_churn_rate_pct\": 8\n }\n}\n\nRevenue Operations Workflows\nWeekly Pipeline Review\n\nUse this workflow for your weekly pipeline inspection cadence.\n\nVerify input data: Confirm pipeline export is current and all required fields (stage, value, close_date, owner) are populated before proceeding.\n\nGenerate pipeline report:\n\npython scripts/pipeline_analyzer.py --input current_pipeline.json --format text\n\n\nCross-check output totals against your CRM source system to confirm data integrity.\n\nReview key indicators:\n\nPipeline coverage ratio (is it above 3x quota?)\nDeals aging beyond threshold (which deals need intervention?)\nConcentration risk (are we over-reliant on a few large deals?)\nStage distribution (is there a healthy funnel shape?)\n\nDocument using template: Use assets/pipeline_review_template.md\n\nAction items: Address aging deals, redistribute pipeline concentration, fill coverage gaps\n\nForecast Accuracy Review\n\nUse monthly or quarterly to evaluate and improve forecasting discipline.\n\nVerify input data: Confirm all forecast periods have corresponding actuals and no periods are missing before running.\n\nGenerate accuracy report:\n\npython scripts/forecast_accuracy_tracker.py forecast_history.json --format text\n\n\nCross-check actuals against closed-won records in your CRM before drawing conclusions.\n\nAnalyze patterns:\n\nIs MAPE trending down (improving)?\nWhich reps or segments have the highest error rates?\nIs there systematic over- or under-forecasting?\n\nDocument using template: Use assets/forecast_report_template.md\n\nImprovement actions: Coach high-bias reps, adjust methodology, improve data hygiene\n\nGTM Efficiency Audit\n\nUse quarterly or during board prep to evaluate go-to-market efficiency.\n\nVerify input data: Confirm revenue, cost, and customer figures reconcile with finance records before running.\n\nCalculate efficiency metrics:\n\npython scripts/gtm_efficiency_calculator.py quarterly_data.json --format text\n\n\nCross-check computed ARR and spend totals against your finance system before sharing results.\n\nBenchmark against targets:\n\nMagic Number (>0.75)\nLTV:CAC (>3:1)\nCAC Payback (<18 months)\nRule of 40 (>40%)\n\nDocument using template: Use assets/gtm_dashboard_template.md\n\nStrategic decisions: Adjust spend allocation, optimize channels, improve retention\n\nQuarterly Business Review\n\nCombine all three tools for a comprehensive QBR analysis.\n\nRun pipeline analyzer for forward-looking coverage\nRun forecast tracker for backward-looking accuracy\nRun GTM calculator for efficiency benchmarks\nCross-reference pipeline health with forecast accuracy\nAlign GTM efficiency metrics with growth targets\nReference Documentation\nReference\tDescription\nRevOps Metrics Guide\tComplete metrics hierarchy, definitions, formulas, and interpretation\nPipeline Management Framework\tPipeline best practices, stage definitions, conversion benchmarks\nGTM Efficiency Benchmarks\tSaaS benchmarks by stage, industry standards, improvement strategies\nTemplates\nTemplate\tUse Case\nPipeline Review Template\tWeekly/monthly pipeline inspection documentation\nForecast Report Template\tForecast accuracy reporting and trend analysis\nGTM Dashboard Template\tGTM efficiency dashboard for leadership review\nSample Pipeline Data\tExample input for pipeline_analyzer.py\nExpected Output\tReference output from pipeline_analyzer.py" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/alirezarezvani/revenue-operations", "publisherUrl": "https://clawhub.ai/alirezarezvani/revenue-operations", "owner": "alirezarezvani", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/revenue-operations", "downloadUrl": "https://openagent3.xyz/downloads/revenue-operations", "agentUrl": "https://openagent3.xyz/skills/revenue-operations/agent", "manifestUrl": "https://openagent3.xyz/skills/revenue-operations/agent.json", "briefUrl": "https://openagent3.xyz/skills/revenue-operations/agent.md" } }