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4asaanAI
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forecast

Generate weighted sales forecasts from pipeline and deal data using stage probabilities.

Updated Aug 23, 2026
Search Tags:#pipeline#gap analysis#scenario analysis#sales forecast#deal probability#revenue forecast#layaa-ai
One-click install
npx skills add https://github.com/4asaanAI/Claude-patches --skill forecast-4asaanai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: forecast
Source: https://github.com/4asaanAI/Claude-patches/tree/main/framework-foundry/Claude%20Plugins/layaa-ai/skills/forecast
Command: npx skills add https://github.com/4asaanAI/Claude-patches --skill forecast-4asaanai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The forecast skill converts scattered pipeline and deal data into a reliable, weighted revenue forecast so teams can set realistic targets, identify gaps, and prioritize deals to hit quota.

Core Features & Use Cases

  • Stage-based probability modeling: Applies Layaa AI conversion benchmarks (MQL→SQL 25%, SQL→Proposal 60%, Proposal→Won 35%) and stage defaults to compute base probabilities.
  • Deal-level adjustments & scenario planning: Adjust probabilities for deal signals (champion, budget, timeline, competition) and produce Best / Expected / Worst case scenarios.
  • Revenue decomposition: Separates implementation (one-time) and retainer (recurring) revenue, models deposits and timing, and provides monthly/quarterly breakdowns and gap analysis to targets.
  • Use Case: Run a quarterly forecast for an Indian SME using Layaa AI pricing tiers to see expected vs target revenue and recommended actions to close the shortfall.

Quick Start

Generate a weighted sales forecast for the next quarter using current pipeline deals, specifying any target and attaching historical close-rate data if available.

Frequently Asked Questions about forecast

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate a weighted sales forecast from pipeline data?▼

To generate a weighted sales forecast, you apply stage-based conversion probabilities to your pipeline records and adjust for deal signals like budget and timeline. This produces Expected, Best, and Worst case revenue scenarios.

What is stage-based probability modeling for revenue forecasting?▼

Stage-based probability modeling calculates base revenue forecasts by applying historical win/loss conversion benchmarks to pipeline deal stages, such as applying default rates for MQL to SQL or Proposal to Won transitions.

Can I use this to run gap analysis against quarterly sales targets?▼

Yes, you can run gap analysis by comparing your weighted expected revenue forecast against specified monthly or quarterly targets. This identifies revenue shortfalls and highlights deals to prioritize to hit quota.

Do I need historical win/loss data to calculate deal probabilities?▼

Historical win/loss metrics are recommended to refine accuracy, but you can use default stage probabilities and Layaa AI conversion benchmarks as a baseline if historical close-rate data is unavailable.

How does revenue decomposition handle one-time and recurring revenue?▼

Revenue decomposition separates one-time implementation revenue from recurring retainer revenue. It models deposit timing and provides monthly or quarterly breakdowns for accurate cash flow forecasting.

What is the best way to adjust deal probabilities for scenario planning?▼

The best way to adjust deal probabilities for scenario planning is to modify base rates using specific deal signals like champion strength, budget availability, timeline urgency, and competitive positioning to generate multiple forecast outcomes.

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