
revenue-operations
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/alirezarezvani/claude-skills --skill revenue-operations- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads revenue-operations automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
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 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:
| 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 |
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:
| 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% |
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
}
}
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
| Reference | Description |
|---|---|
| RevOps Metrics Guide | Complete metrics hierarchy, definitions, formulas, and interpretation |
| Pipeline Management Framework | Pipeline best practices, stage definitions, conversion benchmarks |
| GTM Efficiency Benchmarks | SaaS benchmarks by stage, industry standards, improvement strategies |
Templates
| Template | Use Case |
|---|---|
| Pipeline Review Template | Weekly/monthly pipeline inspection documentation |
| Forecast Report Template | Forecast accuracy reporting and trend analysis |
| GTM Dashboard Template | GTM efficiency dashboard for leadership review |
| Sample Pipeline Data | Example input for pipeline_analyzer.py |
| Expected Output | Reference output from pipeline_analyzer.py |
FAQ
What does the revenue-operations skill do?
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.
How do I install revenue-operations?
Run npx skills add https://github.com/alirezarezvani/claude-skills --skill revenue-operations in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/revenue-operations/ (personal) or .claude/skills/revenue-operations/ (project).
Which AI agents can use revenue-operations?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is revenue-operations safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains revenue-operations?
It is published by alirezarezvani in alirezarezvani/claude-skills. It has 728 recorded installs.
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