Skip to content
revenue-operations · claude-skills · Flowy
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
One skill from claude-skills .
$ npx -y skills add alirezarezvani/claude-skills --skill revenue-operations --agent claude-codeInstalls just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itself Claude auto-loads it when your prompt matches the work. You can call it Invoke it directly when you want it. Slash command /revenue-operations👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
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 📦 Ships with claude-skills
</> SKILL.md
revenue-operations.SKILL.md
---
name : "revenue-operations"
description : 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.
---
# 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
```bash
# 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
Flowy AI Flows that just work. Hand-picked Claude Code plugins that fire the right skill as you prompt.
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:**
```bash
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:**
```json
{
"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:**
```bash
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:**
```json
{
"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:**
```bash
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:**
```json
{
"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.
1. **Verify input data:** Confirm pipeline export is current and all required fields (stage, value, close_date, owner) are populated before proceeding.
2. **Generate pipeline report:**
```bash
python scripts/pipeline_analyzer.py --input current_pipeline.json --format text
```
3. **Cross-check output totals** against your CRM source system to confirm data integrity.
4. **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?)
5. **Document using template:** Use `assets/pipeline_review_template.md`
6. **Action items:** Address aging deals, redistribute pipeline concentration, fill coverage gaps
### Forecast Accuracy Review
Use monthly or quarterly to evaluate and improve forecasting discipline.
1. **Verify input data:** Confirm all forecast periods have corresponding actuals and no periods are missing before running.
2. **Generate accuracy report:**
```bash
python scripts/forecast_accuracy_tracker.py forecast_history.json --format text
```
3. **Cross-check actuals** against closed-won records in your CRM before drawing conclusions.
4. **Analyze patterns:**
- Is MAPE trending down (improving)?
- Which reps or segments have the highest error rates?
- Is there systematic over- or under-forecasting?
5. **Document using template:** Use `assets/forecast_report_template.md`
6. **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.
1. **Verify input data:** Confirm revenue, cost, and customer figures reconcile with finance records before running.
2. **Calculate efficiency metrics:**
```bash
python scripts/gtm_efficiency_calculator.py quarterly_data.json --format text
```
3. **Cross-check computed ARR and spend totals** against your finance system before sharing results.
4. **Benchmark against targets:**
- Magic Number (>0.75)
- LTV:CAC (>3:1)
- CAC Payback (<18 months)
- Rule of 40 (>40%)
5. **Document using template:** Use `assets/gtm_dashboard_template.md`
6. **Strategic decisions:** Adjust spend allocation, optimize channels, improve retention
### Quarterly Business Review
Combine all three tools for a comprehensive QBR analysis.
1. Run pipeline analyzer for forward-looking coverage
2. Run forecast tracker for backward-looking accuracy
3. Run GTM calculator for efficiency benchmarks
4. Cross-reference pipeline health with forecast accuracy
5. Align GTM efficiency metrics with growth targets
---
## Reference Documentation
| Reference | Description |
|-----------|-------------|
| [RevOps Metrics Guide](references/revops-metrics-guide.md) | Complete metrics hierarchy, definitions, formulas, and interpretation |
| [Pipeline Management Framework](references/pipeline-management-framework.md) | Pipeline best practices, stage definitions, conversion benchmarks |
| [GTM Efficiency Benchmarks](references/gtm-efficiency-benchmarks.md) | SaaS benchmarks by stage, industry standards, improvement strategies |
---
## Templates
| Template | Use Case |
|----------|----------|
| [Pipeline Review Template](assets/pipeline_review_template.md) | Weekly/monthly pipeline inspection documentation |
| [Forecast Report Template](assets/forecast_report_template.md) | Forecast accuracy reporting and trend analysis |
| [GTM Dashboard Template](assets/gtm_dashboard_template.md) | GTM efficiency dashboard for leadership review |
| [Sample Pipeline Data](assets/sample_pipeline_data.json) | Example input for pipeline_analyzer.py |
| [Expected Output](assets/expected_output.json) | Reference output from pipeline_analyzer.py |
© 2026 Flowy · Free and open source
Built for Claude Code · Not affiliated with Anthropic
