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Revenue Operations Analyzer

Analyze sales pipeline health, forecast accuracy, and go-to-market efficiency from your data files

Source Download skill
Repository
alirezarezvani/claude-skills
Stars
29,149
Licence
MIT
Files
14 files

What it does

This skill teaches an AI agent to review a sales team's numbers: how much pipeline covers the revenue target, how accurate past revenue forecasts were (using MAPE, a standard percentage error measure), and how efficiently sales and marketing spend turns into recurring revenue (metrics like Magic Number, LTV:CAC, CAC Payback and Net Dollar Retention). It ships three Python scripts that compute these figures from JSON files you provide, plus templates for weekly pipeline reviews and dashboards. Once installed, the agent can run the scripts on your data, explain the results against standard SaaS benchmarks, and fill in report templates.

Great for

  • Revenue or sales operations staff preparing weekly pipeline reviews
  • Finance or founders checking SaaS unit economics and forecast accuracy
  • Analysts who want a quick, repeatable calculation of standard GTM metrics

Skip it if

  • Teams without exported CRM or finance data in the expected JSON format, since the scripts need specific fields and will not work on arbitrary spreadsheets
  • Board-level or audited financial decisions without human review, because the benchmarks and thresholds are generic rules of thumb and the agent may misread inputs
  • Confidential financial data pasted into a cloud agent without checking your company's data policy, since the agent sends conversation content to its provider

Try asking

  • “Analyze this pipeline export and tell me if our Q3 coverage is above 3x quota and which deals are aging”
  • “Calculate our forecast MAPE for the last four quarters and tell me whether we over- or under-forecast”
  • “Compute our Magic Number, LTV:CAC and CAC Payback from this GTM data and rate each one”
  • “Fill in the weekly pipeline review template using the output of the pipeline analyzer”
Step-by-step guide: how to use Revenue Operations AnalyzerInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 14 original files, the MIT licence and an attribution note.

    revenue-operations.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip revenue-operations.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip revenue-operations.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Start with the sample JSON files in assets/ to see the expected input format before using your own CRM exports
  • Ask the agent to cross-check script totals against your CRM, as the skill's own workflow recommends, before acting on results
  • Read the scripts before running them on sensitive data, and run them with --format json when you want output for dashboards

What's inside

  • assets/expected_output.json
  • assets/forecast_report_template.md
  • assets/gtm_dashboard_template.md
  • assets/pipeline_review_template.md
  • assets/sample_forecast_data.json
  • assets/sample_gtm_data.json
  • assets/sample_pipeline_data.json
  • references/gtm-efficiency-benchmarks.md
  • references/pipeline-management-framework.md
  • references/revops-metrics-guide.md
  • scripts/forecast_accuracy_tracker.py
  • scripts/gtm_efficiency_calculator.py
  • scripts/pipeline_analyzer.py
  • SKILL.md

Redistributed unmodified from alirezarezvani/claude-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.