Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Revenue Operations
Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue optimization
skill openclawclawhub Free
Analyzes pipeline coverage, tracks forecast accuracy with MAPE, and calculates GTM efficiency metrics for SaaS revenue optimization
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- Source platform
- Tencent SkillHub
- What's included
- 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
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Trust & source
Release facts
- Source
- Tencent SkillHub
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- Indexed source record
- Version
- 1.0.0
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- Publisher
- alirezarezvani
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Documentation
Revenue Operations
Pipeline analysis, forecast accuracy tracking, and GTM efficiency measurement for SaaS revenue teams. Output formats: All scripts support --format text (human-readable) and --format json (dashboards/integrations).
Quick Start
# Analyze pipeline health and coverage python scripts/pipeline_analyzer.py --input assets/sample_pipeline_data.json --format text # Track forecast accuracy over multiple periods python scripts/forecast_accuracy_tracker.py assets/sample_forecast_data.json --format text # Calculate GTM efficiency metrics python scripts/gtm_efficiency_calculator.py assets/sample_gtm_data.json --format text
1. Pipeline Analyzer
Analyzes sales pipeline health including coverage ratios, stage conversion rates, deal velocity, aging risks, and concentration risks. Input: JSON file with deals, quota, and stage configuration Output: Coverage ratios, conversion rates, velocity metrics, aging flags, risk assessment Usage: python scripts/pipeline_analyzer.py --input pipeline.json --format text Key Metrics Calculated: Pipeline Coverage Ratio -- Total pipeline value / quota target (healthy: 3-4x) Stage Conversion Rates -- Stage-to-stage progression rates Sales Velocity -- (Opportunities x Avg Deal Size x Win Rate) / Avg Sales Cycle Deal Aging -- Flags deals exceeding 2x average cycle time per stage Concentration Risk -- Warns when >40% of pipeline is in a single deal Coverage Gap Analysis -- Identifies quarters with insufficient pipeline Input Schema: { "quota": 500000, "stages": ["Discovery", "Qualification", "Proposal", "Negotiation", "Closed Won"], "average_cycle_days": 45, "deals": [ { "id": "D001", "name": "Acme Corp", "stage": "Proposal", "value": 85000, "age_days": 32, "close_date": "2025-03-15", "owner": "rep_1" } ] }
2. Forecast Accuracy Tracker
Tracks forecast accuracy over time using MAPE, detects systematic bias, analyzes trends, and provides category-level breakdowns. Input: JSON file with forecast periods and optional category breakdowns Output: MAPE score, bias analysis, trends, category breakdown, accuracy rating Usage: python scripts/forecast_accuracy_tracker.py forecast_data.json --format text Key Metrics Calculated: MAPE -- mean(|actual - forecast| / |actual|) x 100 Forecast Bias -- Over-forecasting (positive) vs under-forecasting (negative) tendency Weighted Accuracy -- MAPE weighted by deal value for materiality Period Trends -- Improving, stable, or declining accuracy over time Category Breakdown -- Accuracy by rep, product, segment, or any custom dimension Accuracy Ratings: RatingMAPE RangeInterpretationExcellent<10%Highly predictable, data-driven processGood10-15%Reliable forecasting with minor varianceFair15-25%Needs process improvementPoor>25%Significant forecasting methodology gaps Input Schema: { "forecast_periods": [ {"period": "2025-Q1", "forecast": 480000, "actual": 520000}, {"period": "2025-Q2", "forecast": 550000, "actual": 510000} ], "category_breakdowns": { "by_rep": [ {"category": "Rep A", "forecast": 200000, "actual": 210000}, {"category": "Rep B", "forecast": 280000, "actual": 310000} ] } }
3. GTM Efficiency Calculator
Calculates core SaaS GTM efficiency metrics with industry benchmarking, ratings, and improvement recommendations. Input: JSON file with revenue, cost, and customer metrics Output: Magic Number, LTV:CAC, CAC Payback, Burn Multiple, Rule of 40, NDR with ratings Usage: python scripts/gtm_efficiency_calculator.py gtm_data.json --format text Key Metrics Calculated: MetricFormulaTargetMagic 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% Input Schema: { "revenue": { "current_arr": 5000000, "prior_arr": 3800000, "net_new_arr": 1200000, "arpa_monthly": 2500, "revenue_growth_pct": 31.6 }, "costs": { "sales_marketing_spend": 1800000, "cac": 18000, "gross_margin_pct": 78, "total_operating_expense": 6500000, "net_burn": 1500000, "fcf_margin_pct": 8.4 }, "customers": { "beginning_arr": 3800000, "expansion_arr": 600000, "contraction_arr": 100000, "churned_arr": 300000, "annual_churn_rate_pct": 8 } }
Weekly Pipeline Review
Use this workflow for your weekly pipeline inspection cadence. Verify input data: Confirm pipeline export is current and all required fields (stage, value, close_date, owner) are populated before proceeding. Generate pipeline report: python scripts/pipeline_analyzer.py --input current_pipeline.json --format text Cross-check output totals against your CRM source system to confirm data integrity. Review key indicators: Pipeline coverage ratio (is it above 3x quota?) Deals aging beyond threshold (which deals need intervention?) Concentration risk (are we over-reliant on a few large deals?) Stage distribution (is there a healthy funnel shape?) Document using template: Use assets/pipeline_review_template.md Action items: Address aging deals, redistribute pipeline concentration, fill coverage gaps
Forecast Accuracy Review
Use monthly or quarterly to evaluate and improve forecasting discipline. Verify input data: Confirm all forecast periods have corresponding actuals and no periods are missing before running. Generate accuracy report: python scripts/forecast_accuracy_tracker.py forecast_history.json --format text Cross-check actuals against closed-won records in your CRM before drawing conclusions. Analyze patterns: Is MAPE trending down (improving)? Which reps or segments have the highest error rates? Is there systematic over- or under-forecasting? Document using template: Use assets/forecast_report_template.md Improvement actions: Coach high-bias reps, adjust methodology, improve data hygiene
GTM Efficiency Audit
Use quarterly or during board prep to evaluate go-to-market efficiency. Verify input data: Confirm revenue, cost, and customer figures reconcile with finance records before running. Calculate efficiency metrics: python scripts/gtm_efficiency_calculator.py quarterly_data.json --format text Cross-check computed ARR and spend totals against your finance system before sharing results. Benchmark against targets: Magic Number (>0.75) LTV:CAC (>3:1) CAC Payback (<18 months) Rule of 40 (>40%) Document using template: Use assets/gtm_dashboard_template.md Strategic decisions: Adjust spend allocation, optimize channels, improve retention
Quarterly Business Review
Combine all three tools for a comprehensive QBR analysis. Run pipeline analyzer for forward-looking coverage Run forecast tracker for backward-looking accuracy Run GTM calculator for efficiency benchmarks Cross-reference pipeline health with forecast accuracy Align GTM efficiency metrics with growth targets
Reference Documentation
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
Templates
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
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Config
- SKILL.md
- assets/forecast_report_template.md
- assets/gtm_dashboard_template.md
- assets/pipeline_review_template.md
- assets/expected_output.json
- assets/sample_forecast_data.json