[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-8e17e97f-aa49-4882-be2e-b75d2c21f30e":3,"$fG_py7RYhwKz9WivH25kZbbC7PjGp6fs5ncvQNdzqg-8":43},{"id":4,"title":5,"description":6,"categoryId":7,"moduleId":8,"tags":9,"prompt":10,"icon":11,"source":12,"sourceUrl":13,"authorId":14,"authorName":15,"isPublic":16,"stars":17,"runs":18,"createdAt":19,"updatedAt":19,"module":20,"category":27,"packages":34},"8e17e97f-aa49-4882-be2e-b75d2c21f30e","revenue-operations","分析销售漏斗健康状况、收入预测准确性和上市效率指标，以优化SaaS收入。用于分析销售漏斗覆盖范围、预测收入、评估上市表现、审查销售指标、评估漏斗分析、跟踪预测准确度（使用MAPE）、计算上市效率或衡量SaaS团队的销售额效率和单位经济学。","cat_prod_data","mod_productivity","alirezarezvani,productivity","---\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","","imported","https:\u002F\u002Fgithub.com\u002Falirezarezvani\u002Fclaude-skills","user_system_seed","SkillOPIC",true,93,1362,"2026-05-16 13:49:15",{"id":8,"name":21,"slug":22,"icon":23,"description":24,"sort":25,"createdAt":26},"效率工具","productivity","mdi-lightning-bolt-outline","文档处理、数据分析、自动化工作流",4,"2026-05-16 12:53:40",{"id":7,"name":28,"slug":29,"icon":30,"description":31,"moduleId":8,"sort":32,"skillCount":33,"createdAt":26},"数据分析","data-analysis","mdi-chart-bar","数据可视化、统计分析",2,30,[35],{"id":36,"skillId":4,"version":37,"fileName":38,"fileSize":39,"filePath":40,"fileHash":41,"manifest":42,"createdAt":19},"45c3de52-7a34-4e9a-8b2c-15579651963c","1.0.0","revenue-operations.zip",38190,"uploads\u002Fskills\u002F8e17e97f-aa49-4882-be2e-b75d2c21f30e\u002Frevenue-operations.zip","651a8a5eeb3da5f026678cb9914aeb6f383f44255ee1f36301bef61a33c3d8c2","[{\"path\":\"SKILL.md\",\"isDirectory\":false,\"size\":9435},{\"path\":\"assets\u002Fexpected_output.json\",\"isDirectory\":false,\"size\":2580},{\"path\":\"assets\u002Fforecast_report_template.md\",\"isDirectory\":false,\"size\":3710},{\"path\":\"assets\u002Fgtm_dashboard_template.md\",\"isDirectory\":false,\"size\":5310},{\"path\":\"assets\u002Fpipeline_review_template.md\",\"isDirectory\":false,\"size\":3259},{\"path\":\"assets\u002Fsample_forecast_data.json\",\"isDirectory\":false,\"size\":999},{\"path\":\"assets\u002Fsample_gtm_data.json\",\"isDirectory\":false,\"size\":525},{\"path\":\"assets\u002Fsample_pipeline_data.json\",\"isDirectory\":false,\"size\":3486},{\"path\":\"references\u002Fgtm-efficiency-benchmarks.md\",\"isDirectory\":false,\"size\":10850},{\"path\":\"references\u002Fpipeline-management-framework.md\",\"isDirectory\":false,\"size\":9710},{\"path\":\"references\u002Frevops-metrics-guide.md\",\"isDirectory\":false,\"size\":10151},{\"path\":\"scripts\u002Fforecast_accuracy_tracker.py\",\"isDirectory\":false,\"size\":16889},{\"path\":\"scripts\u002Fgtm_efficiency_calculator.py\",\"isDirectory\":false,\"size\":22585},{\"path\":\"scripts\u002Fpipeline_analyzer.py\",\"isDirectory\":false,\"size\":16174}]",{"code":44,"message":45,"data":46},200,"success",{"items":47,"stats":48,"page":51},[],{"averageRating":49,"totalRatings":49,"ratingCounts":50},0,[49,49,49,49,49],{"limit":52,"offset":49,"hasMore":53,"nextOffset":52,"ratedOnly":16},15,false]