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Basé sur la classification professionnelle SOC
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14 fichiers name revenue-operations description Manage — Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization. Use when analyzing sales pipeline coverage, forecas executor HYBRID skill_id business.business-growth.revenue-operations status ADOPTED security {"level":"standard","pii":false,"approval_required":false} anchors ["business","finance","sales"] tier 2 input_schema [{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true}] output_schema [{"name":"report","type":"string","description":"Analysis report or summary from revenue operations"}]
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
python scripts/pipeline_analyzer.py --input assets/sample_pipeline_data.json --format text
python scripts/forecast_accuracy_tracker.py assets/sample_forecast_data.json --format text
python scripts/gtm_efficiency_calculator.py assets/sample_gtm_data.json --format text
Tools Overview
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 ratesSales Velocity -- (Opportunities x Avg Deal Size x Win Rate) / Avg Sales CycleDeal Aging -- Flags deals exceeding 2x average cycle time per stageConcentration Risk -- Warns when >40% of pipeline is in a single dealCoverage 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
python scripts/forecast_accuracy_tracker.py forecast_data.json --format text
MAPE -- mean(|actual - forecast| / |actual|) x 100Forecast Bias -- Over-forecasting (positive) vs under-forecasting (negative) tendencyWeighted Accuracy -- MAPE weighted by deal value for materialityPeriod Trends -- Improving, stable, or declining accuracy over timeCategory Breakdown -- Accuracy by rep, product, segment, or any custom dimension Rating MAPE Range Interpretation Excellent <10% Highly predictable, data-driven process Good 10-15% Reliable forecasting with minor variance Fair 15-25% Needs process improvement Poor >25% Significant forecasting methodology gaps
{
"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
python scripts/gtm_efficiency_calculator.py gtm_data.json --format text
Metric Formula Target Magic Number Net New ARR / Prior Period S&M Spend >0.75 LTV:CAC (ARPA x Gross Margin / Churn Rate) / CAC >3:1 CAC Payback CAC / (ARPA x Gross Margin) months <18 months Burn Multiple Net Burn / Net New ARR <2x Rule of 40 Revenue Growth % + FCF Margin % >40% Net Dollar Retention (Begin ARR + Expansion - Contraction - Churn) / Begin ARR >110%
{
"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
}
}
Revenue Operations Workflows
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
Templates
Why This Skill Exists Manage — Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization.
When to Use Use this skill when analyzing sales pipeline coverage, forecas
What If Fails If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.
