Forecast company-level revenue from aggregate monthly and quarterly data.
npx skills add https://github.com/jikig-ai/soleur --skill revenue-analyst
Please help me install this Agent Skill. Skill: revenue-analyst Source: https://github.com/jikig-ai/soleur/tree/main/.openhands/skills/revenue-analyst Command: npx skills add https://github.com/jikig-ai/soleur --skill revenue-analyst
Many founders and finance teams lack a concise, repeatable process to turn aggregate revenue numbers into actionable forecasts, P&L projections, and trend insights; this Skill standardizes company-level revenue modeling so planning decisions are faster and more consistent.
Create a three-year revenue forecast from the last 24 months of monthly revenue broken out by product with base and aggressive scenarios and a one-page summary dashboard.
High-intent search queries and answers about installing and using this skill.
You can forecast revenue from monthly aggregate data by building top-down projections and scenario modeling using historical inputs. The process computes growth rates, MRR/ARR summaries, and confidence ranges for a three-year revenue forecast.
The best way to build a P&L projection from aggregate revenue numbers is by applying standardized financial modeling to compute margin breakdowns and break-even calculations. This approach produces profit and loss projections directly from aggregate inputs.
Scenario planning for revenue forecasts works by generating base, downside, and aggressive upside cases from historical data. Sensitivity analysis is applied to produce scenario-based forecasts with identified risks and confidence ranges for planning decisions.
Yes, you can compute MRR and ARR summaries from quarterly revenue data. The revenue tracking process summarizes monthly or quarterly revenue, calculates growth rates, and reports MRR, ARR, and churn metrics for financial planning.
Yes, trend analysis identifies seasonality, cohort revenue behavior, and segment or product revenue contributions. It processes aggregate revenue data to highlight behavioral trends and segment performance for company financial planning.
Top-down revenue forecasting relies on aggregate revenue data rather than granular transactional inputs, which limits cohort-level precision. It is best suited for standardized company-level planning rather than individual customer-level revenue predictions.
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