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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
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직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/thiagofernandes1987-create/APEX --skill revenue-operations명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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Token-aware reasoning workflow with real tools: picks an operating mode to control cost, runs a structured pipeline (decompose → validate → verify → snapshot), and gives Claude Program-of-Thought, RK4/Euler, a code gate, and a safe skill router. Use when: multi-step or high-stakes tasks, real math, precise computation, audits, or the user mentions APEX, PoT, pipeline, or scientific mode.
**v00.33.0**: Ingested from antigravity-awesome-skills community repo
run multiple local CLI agents in parallel (separate tmux sessions)
SOC 직업 분류 기준
SKILL.md 표시 중
| 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"}] |
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).
# 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
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:
Input Schema:
{
"quota": 500000,
"stages": ["Discovery",
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:
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}
]
}
}
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
}
}
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:
Document using template: Use assets/pipeline_review_template.md
Action items: Address aging deals, redistribute pipeline concentration, fill coverage gaps
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:
Document using template: Use assets/forecast_report_template.md
Improvement actions: Coach high-bias reps, adjust methodology, improve data hygiene
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:
Document using template: Use assets/gtm_dashboard_template.md
Strategic decisions: Adjust spend allocation, optimize channels, improve retention
Combine all three tools for a comprehensive QBR analysis.
| 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 |
| 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 |
Manage — Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization.
Use this skill when analyzing sales pipeline coverage, forecas
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.