mcpField
F
forecast-modeling

forecast-modeling

github.com/gtmagentsData & databases42872 installsUpdated 30w ago
Repository

About forecast-modeling

forecast-modeling is a skill for designing and reviewing forecasting methods, assumptions, and scenarios. It is intended for teams establishing forecast cycles, planning budgets, or comparing forecasts with pipeline and actual results.

Key features

  1. 01Bottom-up, top-down, cohort, and blended methods
  2. 02Mapping business drivers to KPIs
  3. 03Tunable base, upside, and downside scenarios
  4. 04Data sources, refresh, validation, and ownership governance
  5. 05Driver trees, scenario sheets, and QA checklists

Use cases

  1. 01Setting up or revising a forecasting cadence
  2. 02Planning scenarios before board meetings or budget cycles
  3. 03Auditing forecast differences against pipeline and actuals

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gtmagents/gtm-agents --skill forecast-modeling

After installing, type/forecast-modelingin Claude Code to confirm it loaded.

Details

From the project README

Forecast Modeling System Skill

When to Use

  • Launching new forecasting cadences or revisiting methodology.
  • Running scenario planning ahead of board meetings or budget cycles.
  • Auditing deviations between forecast, pipeline, and actuals.

Framework

  1. Method Selection – pick bottom-up CRM, top-down macro, cohort, or blended models and document assumptions.
  2. Driver Mapping – define win rates, velocity, expansion, churn, pricing, and seasonality inputs.
  3. Scenario Logic – establish base/upside/downside cases with tunable levers for sensitivity analysis.
  4. Model Governance – list data sources, refresh cadence, validation checks, and ownership.
  5. Output Packaging – standardize tables, charts, and narrative prompts for exec review.

Templates

  • Driver tree diagram connecting levers to KPIs.
  • Scenario sheet (assumption → base/upside/downside values).
  • Model QA checklist (data freshness, formula audits, version history).

Tips

  • Keep raw inputs + assumptions in version control for auditability.
  • Pair with variance-analysis skill to recalibrate after each cycle.
  • Automate sensitivity runs to answer "what-if" questions during reviews.

Comments

Markdown supported