[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7199AV2V4WYT1SWNWJZYAS2":3,"skill-same:01M7199AV2V4WYT1SWNWJZYAS2":123},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":48,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M7199AV2V4WYT1SWNWJZYAS2","revenue-operations",null,"Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization. Use when analyzing sales pipeline coverage, forecasting revenue, evaluating go-to-market performance, reviewing sales metrics, assessing pipeline analysis, tracking forecast accuracy with MAPE, calculating GTM efficiency, or measuring sales efficiency and unit economics for SaaS teams.","dahaliztupe-sketch\u002FKalmeron-Two","dahaliztupe-sketch","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F271085317?v=4","01M719765Q83P08JCQ1SWNQR0N","01M719766HQBGF44W90MPJKTD2",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"revenue operations","operations","revenue","for","sales pipeline","forecast","forecast accuracy","metrics","go","analyzing","go-to-market","gtm","efficiency","analysis","when","performance","pipeline","economics","unit","coverage","2026-11-10 10:12:30",false,"---\nname: \"revenue-operations\"\ndescription: Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization. Use when analyzing sales pipeline coverage, forecasting revenue, evaluating go-to-market performance, reviewing sales metrics, assessing pipeline analysis, tracking forecast accuracy with MAPE, calculating GTM efficiency, or measuring sales efficiency and unit economics for SaaS teams.\n---\n\n# Revenue Operations\n\nPipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams.\n\n> **Output formats:** All scripts support `--format text` (human-readable) and `--format json` (dashboards\u002Fintegrations).\n\n---\n\n## Quick Start\n\n```bash\n# Analyze pipeline health and coverage\npython scripts\u002Fpipeline_analyzer.py --input assets\u002Fsample_pipeline_data.json --format text\n\n# Track forecast accuracy over multiple periods\npython scripts\u002Fforecast_accuracy_tracker.py assets\u002Fsample_forecast_data.json --format text\n\n# Calculate GTM efficiency metrics\npython scripts\u002Fgtm_efficiency_calculator.py assets\u002Fsample_gtm_data.json --format text\n```\n\n---\n\n## Tools Overview\n\n### 1. Pipeline Analyzer\n\nAnalyzes sales pipeline health including coverage ratios, stage conversion rates, deal velocity, aging risks, and concentration risks.\n\n**Input:** JSON file with deals, quota, and stage configuration\n**Output:** Coverage ratios, conversion rates, velocity metrics, aging flags, risk assessment\n\n**Usage:**\n\n```bash\npython scripts\u002Fpipeline_analyzer.py --input pipeline.json --format text\n```\n\n**Key Metrics Calculated:**\n- **Pipeline Coverage Ratio** -- Total pipeline value \u002F quota target (healthy: 3-4x)\n- **Stage Conversion Rates** -- Stage-to-stage progression rates\n- **Sales Velocity** -- (Opportunities x Avg Deal Size x Win Rate) \u002F Avg Sales Cycle\n- **Deal Aging** -- Flags deals exceeding 2x average cycle time per stage\n- **Concentration Risk** -- Warns when >40% of pipeline is in a single deal\n- **Coverage Gap Analysis** -- Identifies quarters with insufficient pipeline\n\n**Input Schema:**\n\n```json\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```\n\n### 2. Forecast Accuracy Tracker\n\nTracks forecast accuracy over time using MAPE, detects systematic bias, analyzes trends, and provides category-level breakdowns.\n\n**Input:** JSON file with forecast periods and optional category breakdowns\n**Output:** MAPE score, bias analysis, trends, category breakdown, accuracy rating\n\n**Usage:**\n\n```bash\npython scripts\u002Fforecast_accuracy_tracker.py forecast_data.json --format text\n```\n\n**Key Metrics Calculated:**\n- **MAPE** -- mean(|actual - forecast| \u002F |actual|) x 100\n- **Forecast Bias** -- Over-forecasting (positive) vs under-forecasting (negative) tendency\n- **Weighted Accuracy** -- MAPE weighted by deal value for materiality\n- **Period Trends** -- Improving, stable, or declining accuracy over time\n- **Category Breakdown** -- Accuracy by rep, product, segment, or any custom dimension\n\n**Accuracy Ratings:**\n| Rating | MAPE Range | Interpretation |\n|--------|-----------|----------------|\n| Excellent | \u003C10% | Highly predictable, data-driven process |\n| Good | 10-15% | Reliable forecasting with minor variance |\n| Fair | 15-25% | Needs process improvement |\n| Poor | >25% | Significant forecasting methodology gaps |\n\n**Input Schema:**\n\n```json\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```\n\n### 3. GTM Efficiency Calculator\n\nCalculates core SaaS GTM efficiency metrics with industry benchmarking, ratings, and improvement recommendations.\n\n**Input:** JSON file with revenue, cost, and customer metrics\n**Output:** Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR with ratings\n\n**Usage:**\n\n```bash\npython scripts\u002Fgtm_efficiency_calculator.py gtm_data.json --format text\n```\n\n**Key Metrics Calculated:**\n\n| Metric | Formula | Target |\n|--------|---------|--------|\n| Magic Number | Net New ARR \u002F Prior Period S&M Spend | >0.75 |\n| LTV:CAC | (ARPA x Gross Margin \u002F Churn Rate) \u002F CAC | >3:1 |\n| CAC Payback | CAC \u002F (ARPA x Gross Margin) months | \u003C18 months |\n| Burn Multiple | Net Burn \u002F Net New ARR | \u003C2x |\n| Rule of 40 | Revenue Growth % + FCF Margin % | >40% |\n| Net Dollar Retention | (Begin ARR + Expansion - Contraction - Churn) \u002F Begin ARR | >110% |\n\n**Input Schema:**\n\n```json\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```\n\n---\n\n## Revenue Operations Workflows\n\n### Weekly Pipeline Review\n\nUse this workflow for your weekly pipeline inspection cadence.\n\n1. **Verify input data:** Confirm pipeline export is current and all required fields (stage, value, close_date, owner) are populated before proceeding.\n\n2. **Generate pipeline report:**\n   ```bash\n   python scripts\u002Fpipeline_analyzer.py --input current_pipeline.json --format text\n   ```\n\n3. **Cross-check output totals** against your CRM source system to confirm data integrity.\n\n4. **Review key indicators:**\n   - Pipeline coverage ratio (is it above 3x quota?)\n   - Deals aging beyond threshold (which deals need intervention?)\n   - Concentration risk (are we over-reliant on a few large deals?)\n   - Stage distribution (is there a healthy funnel shape?)\n\n5. **Document using template:** Use `assets\u002Fpipeline_review_template.md`\n\n6. **Action items:** Address aging deals, redistribute pipeline concentration, fill coverage gaps\n\n### Forecast Accuracy Review\n\nUse monthly or quarterly to evaluate and improve forecasting discipline.\n\n1. **Verify input data:** Confirm all forecast periods have corresponding actuals and no periods are missing before running.\n\n2. **Generate accuracy report:**\n   ```bash\n   python scripts\u002Fforecast_accuracy_tracker.py forecast_history.json --format text\n   ```\n\n3. **Cross-check actuals** against closed-won records in your CRM before drawing conclusions.\n\n4. **Analyze patterns:**\n   - Is MAPE trending down (improving)?\n   - Which reps or segments have the highest error rates?\n   - Is there systematic over- or under-forecasting?\n\n5. **Document using template:** Use `assets\u002Fforecast_report_template.md`\n\n6. **Improvement actions:** Coach high-bias reps, adjust methodology, improve data hygiene\n\n### GTM Efficiency Audit\n\nUse quarterly or during board prep to evaluate go-to-market efficiency.\n\n1. **Verify input data:** Confirm revenue, cost, and customer figures reconcile with finance records before running.\n\n2. **Calculate efficiency metrics:**\n   ```bash\n   python scripts\u002Fgtm_efficiency_calculator.py quarterly_data.json --format text\n   ```\n\n3. **Cross-check computed ARR and spend totals** against your finance system before sharing results.\n\n4. **Benchmark against targets:**\n   - Magic Number (>0.75)\n   - LTV:CAC (>3:1)\n   - CAC Payback (\u003C18 months)\n   - Rule of 40 (>40%)\n\n5. **Document using template:** Use `assets\u002Fgtm_dashboard_template.md`\n\n6. **Strategic decisions:** Adjust spend allocation, optimize channels, improve retention\n\n### Quarterly Business Review\n\nCombine all three tools for a comprehensive QBR analysis.\n\n1. Run pipeline analyzer for forward-looking coverage\n2. Run forecast tracker for backward-looking accuracy\n3. Run GTM calculator for efficiency benchmarks\n4. Cross-reference pipeline health with forecast accuracy\n5. Align GTM efficiency metrics with growth targets\n\n---\n\n## Reference Documentation\n\n| Reference | Description |\n|-----------|-------------|\n| [RevOps Metrics Guide](references\u002Frevops-metrics-guide.md) | Complete metrics hierarchy, definitions, formulas, and interpretation |\n| [Pipeline Management Framework](references\u002Fpipeline-management-framework.md) | Pipeline best practices, stage definitions, conversion benchmarks |\n| [GTM Efficiency Benchmarks](references\u002Fgtm-efficiency-benchmarks.md) | SaaS benchmarks by stage, industry standards, improvement strategies |\n\n---\n\n## Templates\n\n| Template | Use Case |\n|----------|----------|\n| [Pipeline Review Template](assets\u002Fpipeline_review_template.md) | Weekly\u002Fmonthly pipeline inspection documentation |\n| [Forecast Report Template](assets\u002Fforecast_report_template.md) | Forecast accuracy reporting and trend analysis |\n| [GTM Dashboard Template](assets\u002Fgtm_dashboard_template.md) | GTM efficiency dashboard for leadership review |\n| [Sample Pipeline Data](assets\u002Fsample_pipeline_data.json) | Example input for pipeline_analyzer.py |\n| [Expected Output](assets\u002Fexpected_output.json) | Reference output from pipeline_analyzer.py |\n","# Revenue Operations\n\nPipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams.\n\n> **Output formats:** All scripts support `--format text` (human-readable) and `--format json` (dashboards\u002Fintegrations).\n\n---\n\n## Quick Start\n\n```bash\n# Analyze pipeline health and coverage\npython scripts\u002Fpipeline_analyzer.py --input assets\u002Fsample_pipeline_data.json --format text\n\n# Track forecast accuracy over multiple periods\npython scripts\u002Fforecast_accuracy_tracker.py assets\u002Fsample_forecast_data.json --format text\n\n# Calculate GTM efficiency metrics\npython scripts\u002Fgtm_efficiency_calculator.py assets\u002Fsample_gtm_data.json --format text\n```\n\n---\n\n## Tools Overview\n\n### 1. Pipeline Analyzer\n\nAnalyzes sales pipeline health including coverage ratios, stage conversion rates, deal velocity, aging risks, and concentration risks.\n\n**Input:** JSON file with deals, quota, and stage configuration\n**Output:** Coverage ratios, conversion rates, velocity metrics, aging flags, risk assessment\n\n**Usage:**\n\n```bash\npython scripts\u002Fpipeline_analyzer.py --input pipeline.json --format text\n```\n\n**Key Metrics Calculated:**\n- **Pipeline Coverage Ratio** -- Total pipeline value \u002F quota target (healthy: 3-4x)\n- **Stage Conversion Rates** -- Stage-to-stage progression rates\n- **Sales Velocity** -- (Opportunities x Avg Deal Size x Win Rate) \u002F Avg Sales Cycle\n- **Deal Aging** -- Flags deals exceeding 2x average cycle time per stage\n- **Concentration Risk** -- Warns when >40% of pipeline is in a single deal\n- **Coverage Gap Analysis** -- Identifies quarters with insufficient pipeline\n\n**Input Schema:**\n\n```json\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```\n\n### 2. Forecast Accuracy Tracker\n\nTracks forecast accuracy over time using MAPE, detects systematic bias, analyzes trends, and provides category-level breakdowns.\n\n**Input:** JSON file with forecast periods and optional category breakdowns\n**Output:** MAPE score, bias analysis, trends, category breakdown, accuracy rating\n\n**Usage:**\n\n```bash\npython scripts\u002Fforecast_accuracy_tracker.py forecast_data.json --format text\n```\n\n**Key Metrics Calculated:**\n- **MAPE** -- mean(|actual - forecast| \u002F |actual|) x 100\n- **Forecast Bias** -- Over-forecasting (positive) vs under-forecasting (negative) tendency\n- **Weighted Accuracy** -- MAPE weighted by deal value for materiality\n- **Period Trends** -- Improving, stable, or declining accuracy over time\n- **Category Breakdown** -- Accuracy by rep, product, segment, or any custom dimension\n\n**Accuracy Ratings:**\n| Rating | MAPE Range | Interpretation |\n|--------|-----------|----------------|\n| Excellent | \u003C10% | Highly predictable, data-driven process |\n| Good | 10-15% | Reliable forecasting with minor variance |\n| Fair | 15-25% | Needs process improvement |\n| Poor | >25% | Significant forecasting methodology gaps |\n\n**Input Schema:**\n\n```json\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```\n\n### 3. GTM Efficiency Calculator\n\nCalculates core SaaS GTM efficiency metrics with industry benchmarking, ratings, and improvement recommendations.\n\n**Input:** JSON file with revenue, cost, and customer metrics\n**Output:** Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR with ratings\n\n**Usage:**\n\n```bash\npython scripts\u002Fgtm_efficiency_calculator.py gtm_data.json --format text\n```\n\n**Key Metrics Calculated:**\n\n| Metric | Formula | Target |\n|--------|---------|--------|\n| Magic Number | Net New ARR \u002F Prior Period S&M Spend | >0.75 |\n| LTV:CAC | (ARPA x Gross Margin \u002F Churn Rate) \u002F CAC | >3:1 |\n| CAC Payback | CAC \u002F (ARPA x Gross Margin) months | \u003C18 months |\n| Burn Multiple | Net Burn \u002F Net New ARR | \u003C2x |\n| Rule of 40 | Revenue Growth % + FCF Margin % | >40% |\n| Net Dollar Retention | (Begin ARR + Expansion - Contraction - Churn) \u002F Begin ARR | >110% |\n\n**Input Schema:**\n\n```json\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```\n\n---\n\n## Revenue Operations Workflows\n\n### Weekly Pipeline Review\n\nUse this workflow for your weekly pipeline inspection cadence.\n\n1. **Verify input data:** Confirm pipeline export is current and all required fields (stage, value, close_date, owner) are populated before proceeding.\n\n2. **Generate pipeline report:**\n   ```bash\n   python scripts\u002Fpipeline_analyzer.py --input current_pipeline.json --format text\n   ```\n\n3. **Cross-check output totals** against your CRM source system to confirm data integrity.\n\n4. **Review key indicators:**\n   - Pipeline coverage ratio (is it above 3x quota?)\n   - Deals aging beyond threshold (which deals need intervention?)\n   - Concentration risk (are we over-reliant on a few large deals?)\n   - Stage distribution (is there a healthy funnel shape?)\n\n5. **Document using template:** Use `assets\u002Fpipeline_review_template.md`\n\n6. **Action items:** Address aging deals, redistribute pipeline concentration, fill coverage gaps\n\n### Forecast Accuracy Review\n\nUse monthly or quarterly to evaluate and improve forecasting discipline.\n\n1. **Verify input data:** Confirm all forecast periods have corresponding actuals and no periods are missing before running.\n\n2. **Generate accuracy report:**\n   ```bash\n   python scripts\u002Fforecast_accuracy_tracker.py forecast_history.json --format text\n   ```\n\n3. **Cross-check actuals** against closed-won records in your CRM before drawing conclusions.\n\n4. **Analyze patterns:**\n   - Is MAPE trending down (improving)?\n   - Which reps or segments have the highest error rates?\n   - Is there systematic over- or under-forecasting?\n\n5. **Document using template:** Use `assets\u002Fforecast_report_template.md`\n\n6. **Improvement actions:** Coach high-bias reps, adjust methodology, improve data hygiene\n\n### GTM Efficiency Audit\n\nUse quarterly or during board prep to evaluate go-to-market efficiency.\n\n1. **Verify input data:** Confirm revenue, cost, and customer figures reconcile with finance records before running.\n\n2. **Calculate efficiency metrics:**\n   ```bash\n   python scripts\u002Fgtm_efficiency_calculator.py quarterly_data.json --format text\n   ```\n\n3. **Cross-check computed ARR and spend totals** against your finance system before sharing results.\n\n4. **Benchmark against targets:**\n   - Magic Number (>0.75)\n   - LTV:CAC (>3:1)\n   - CAC Payback (\u003C18 months)\n   - Rule of 40 (>40%)\n\n5. **Document using template:** Use `assets\u002Fgtm_dashboard_template.md`\n\n6. **Strategic decisions:** Adjust spend allocation, optimize channels, improve retention\n\n### Quarterly Business Review\n\nCombine all three tools for a comprehensive QBR analysis.\n\n1. Run pipeline analyzer for forward-looking coverage\n2. Run forecast tracker for backward-looking accuracy\n3. Run GTM calculator for efficiency benchmarks\n4. Cross-reference pipeline health with forecast accuracy\n5. Align GTM efficiency metrics with growth targets\n\n---\n\n## Reference Documentation\n\n| Reference | Description |\n|-----------|-------------|\n| [RevOps Metrics Guide](references\u002Frevops-metrics-guide.md) | Complete metrics hierarchy, definitions, formulas, and interpretation |\n| [Pipeline Management Framework](references\u002Fpipeline-management-framework.md) | Pipeline best practices, stage definitions, conversion benchmarks |\n| [GTM Efficiency Benchmarks](references\u002Fgtm-efficiency-benchmarks.md) | SaaS benchmarks by stage, industry standards, improvement strategies |\n\n---\n\n## Templates\n\n| Template | Use Case |\n|----------|----------|\n| [Pipeline Review Template](assets\u002Fpipeline_review_template.md) | Weekly\u002Fmonthly pipeline inspection documentation |\n| [Forecast Report Template](assets\u002Fforecast_report_template.md) | Forecast accuracy reporting and trend analysis |\n| [GTM Dashboard Template](assets\u002Fgtm_dashboard_template.md) | GTM efficiency dashboard for leadership review |\n| [Sample Pipeline Data](assets\u002Fsample_pipeline_data.json) | Example input for pipeline_analyzer.py |\n| [Expected Output](assets\u002Fexpected_output.json) | Reference output from pipeline_analyzer.py |\n",{"name":5,"description":7},"main","13d9afadbb6a87d6209fe4bd6ca2bbe4553119ac",".agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations",0,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two","https:\u002F\u002Fkalmeron-two.vercel.app","https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch","2026-10-05 12:24:53",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add dahaliztupe-sketch\u002FKalmeron-Two --skill revenue-operations",{"tool":57,"command":58},"bunx","bunx skills add dahaliztupe-sketch\u002FKalmeron-Two --skill revenue-operations",{"tool":60,"command":61},"pnpm","pnpm dlx skills add dahaliztupe-sketch\u002FKalmeron-Two --skill revenue-operations","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002FSKILL.md",true,[65,71,75,79,83,87,91,95,101,105,109,115,119],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"assets\u002Fexpected_output.json","ASSET","资产文件",2580,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fassets\u002Fexpected_output.json",{"path":72,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":73,"sourceHtmlUrl":74},"assets\u002Fforecast_report_template.md",3710,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fassets\u002Fforecast_report_template.md",{"path":76,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":77,"sourceHtmlUrl":78},"assets\u002Fgtm_dashboard_template.md",5310,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fassets\u002Fgtm_dashboard_template.md",{"path":80,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":81,"sourceHtmlUrl":82},"assets\u002Fpipeline_review_template.md",3259,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fassets\u002Fpipeline_review_template.md",{"path":84,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":85,"sourceHtmlUrl":86},"assets\u002Fsample_forecast_data.json",999,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fassets\u002Fsample_forecast_data.json",{"path":88,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":89,"sourceHtmlUrl":90},"assets\u002Fsample_gtm_data.json",525,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fassets\u002Fsample_gtm_data.json",{"path":92,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":93,"sourceHtmlUrl":94},"assets\u002Fsample_pipeline_data.json",3486,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fassets\u002Fsample_pipeline_data.json",{"path":96,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fgtm-efficiency-benchmarks.md","REFERENCE","参考文档",10850,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Freferences\u002Fgtm-efficiency-benchmarks.md",{"path":102,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fpipeline-management-framework.md",9710,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Freferences\u002Fpipeline-management-framework.md",{"path":106,"resourceType":97,"resourceTypeDesc":98,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Frevops-metrics-guide.md",10151,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Freferences\u002Frevops-metrics-guide.md",{"path":110,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":113,"sourceHtmlUrl":114},"scripts\u002Fforecast_accuracy_tracker.py","SCRIPT","脚本",16889,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fscripts\u002Fforecast_accuracy_tracker.py",{"path":116,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":117,"sourceHtmlUrl":118},"scripts\u002Fgtm_efficiency_calculator.py",22585,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fscripts\u002Fgtm_efficiency_calculator.py",{"path":120,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":121,"sourceHtmlUrl":122},"scripts\u002Fpipeline_analyzer.py",16174,"https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fbusiness-growth-skills\u002Frevenue-operations\u002Frevenue-operations\u002Fscripts\u002Fpipeline_analyzer.py",[124,141,166,184,213,242],{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":128,"sourceHtmlUrl":129,"categories":130,"keywords":136,"publishedAt":38},"01M719SWYD9RGS97HJ69PXWY62","add-new-agent","خطوات تفصيليّة لإضافة وكيل ذكاء اصطناعي جديد إلى منصّة Kalmeron من الفكرة إلى الإنتاج","https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002FSKILL.md",[131],{"categoryNo":132,"categoryName":133,"taxonomyType":134,"taxonomyTypeDesc":135},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[137,138,139,140],"add","add new agent","new","new agent",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":158,"publishedAt":38},"01M719SSKNBA7T5NS7V6DP0PX4","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwriting-skills\u002FSKILL.md",[147,150,153],{"categoryNo":148,"categoryName":149,"taxonomyType":134,"taxonomyTypeDesc":135},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":151,"categoryName":152,"taxonomyType":134,"taxonomyTypeDesc":135},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":154,"categoryName":155,"taxonomyType":156,"taxonomyTypeDesc":157},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[159,160,139,32,161,162,163,164,165],"writing skills","writing","work","editing","deployment","use","Existing",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":174,"publishedAt":38},"01M719SRFDNKA3044QD3FYZ05R","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwriting-plans\u002FSKILL.md",[172,173],{"categoryNo":151,"categoryName":152,"taxonomyType":134,"taxonomyTypeDesc":135},{"categoryNo":154,"categoryName":155,"taxonomyType":156,"taxonomyTypeDesc":157},[175,160,176,177,178,179,32,164,21,180,181,182,183],"writing plans","plans","code","requirements","you","task","multi","spec","multi-step task",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":196,"publishedAt":38},"01M719SPN5FM1JYJSR9DGJQPZ0","web-design-guidelines","Review UI code for Web Interface Guidelines compliance. Use when asked to \"review my UI\", \"check accessibility\", \"audit design\", \"review UX\", or \"check my site against best practices\".","https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweb-design-guidelines\u002FSKILL.md",[190,193],{"categoryNo":191,"categoryName":192,"taxonomyType":134,"taxonomyTypeDesc":135},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":194,"categoryName":195,"taxonomyType":156,"taxonomyTypeDesc":157},"01JBSEEDCAT000000000000032","设计师",[197,198,199,200,201,202,203,204,205,206,207,177,21,208,209,210,164,32,211,212],"web design","web design guidelines","web","design","guidelines","review","accessibility","ux","audit","compliance","check","vercel","interface","web interface guidelines","best practices","file",{"skillNo":214,"name":215,"nameZh":6,"description":216,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":217,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":224,"publishedAt":38},"01M719SK11T05QYVPTWWYMYY5B","vitest","Set up Vitest testing in any project — detects project type (Cloudflare Workers, React, Node), generates vitest.config.ts, test setup, utilities, and sample tests. Also covers mocking patterns, coverage config, workspace setup, and Jest migration. Trigger with 'set up vitest', 'add tests', 'configure vitest', 'migrate from jest', 'vitest config', 'add unit tests', or 'testing setup'.","MIT","https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fvitest\u002FSKILL.md",[220,221],{"categoryNo":148,"categoryName":149,"taxonomyType":134,"taxonomyTypeDesc":135},{"categoryNo":222,"categoryName":223,"taxonomyType":134,"taxonomyTypeDesc":135},"01JBSEEDCAT000000000000009","前端开发",[215,225,226,227,228,229,230,231,232,233,234,235,236,237,238,36,239,37,240,241],"cloudflare","cloudflare workers","config","node","sample","react","jest","migrate","configure","Utilities","up","testing","workspace setup","project","SET","test setup","patterns",{"skillNo":243,"name":244,"nameZh":6,"description":245,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":246,"categories":247,"keywords":248,"publishedAt":38},"01M719SGV9R1N88QWAB7Y925HD","verification-loops","Use when about to claim work is complete, fixed, or passing, before committing or creating PRs. Requires running verification commands and confirming output before making any success claims. Evidence before assertions always.","https:\u002F\u002Fgithub.com\u002Fdahaliztupe-sketch\u002FKalmeron-Two\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fverification-loops\u002FSKILL.md",[],[249,250,251,252,164,253,254,255,256,257,258,259,161,260,261,32],"loops","verification","verification loops","claim","assertions","prs","complete","evidence","running","commands","about","claims","output"]