forecast-skill

Generates weighted sales revenue forecasts with risk flags, commit recommendations, and gap-to-quota analysis.

Updated Aug 12, 2026
One-click install
npx skills add https://github.com/innFactory-AI/company-ai-stack-skills --skill forecast-skill-innfactory-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: forecast-skill
Source: https://github.com/innFactory-AI/company-ai-stack-skills/tree/main/de/skills/sales/forecast-skill
Command: npx skills add https://github.com/innFactory-AI/company-ai-stack-skills --skill forecast-skill-innfactory-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Sales teams struggle to turn raw CRM pipeline data into a defensible revenue forecast. This Skill structures pipeline data into weighted projections with evidence-based deal categorization, risk flagging, and gap-to-quota analysis so forecast calls are grounded in data rather than optimism. ## Core Features & Use Cases - Deal Categorization: Assigns every opportunity to Closed Won, Commit, Best Case, Pipeline, or Omit using an evidence-based decision tree. - Weighted Projections: Applies stage-based probabilities derived from historical win rates or evidence-backed confidence estimates, never prescribing arbitrary conversion rates. - Risk Flagging & Gap Analysis: Detects risks like slipped close dates, single-threaded deals, and stale engagement, then computes commit gaps, coverage ratios, and pipeline generation needs. - Use Case: Before a quarterly forecast call, connect your CRM via MCP or upload a pipeline CSV, and receive a formatted forecast summary with category totals, a risk report, and the most movable deals to close the quota gap. ## Quick Start Create a forecast for this quarter using my uploaded pipeline export and quota target.

Frequently Asked Questions about forecast-skill

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

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

Provide pipeline deals with values, stages, and close dates from your CRM via MCP or a CSV/XLSX upload. The skill categorizes each deal, applies stage-based or confidence-based weighting derived from your historical win rates, and outputs a weighted forecast summary.

What forecast categories should I use for pipeline deals?▼

Use five evidence-based categories: Closed Won, Commit, Best Case, Pipeline, and Omit. Each deal is assigned via a decision tree requiring concrete evidence such as signed contracts, verbal commitments, confirmed budget, and decision-maker engagement.

Can I use this forecast skill without a connected CRM?▼

Yes, the skill works without an integrated data source. Provide pipeline data directly in chat or upload CSV/XLSX files containing deal names, values, stages, close dates, and quota targets, and the same forecasting workflow applies.

How is pipeline coverage ratio calculated for quota analysis?▼

Coverage ratio divides total active pipeline value by the quota target, with weighted and quality-adjusted variants that remove high-risk and omitted deals. The result is compared against your historical coverage-to-close ratio to judge sufficiency.

Does the skill prescribe standard win rates or conversion benchmarks?▼

No, it never prescribes conversion rates or stage probabilities. All probabilities must be derived from your own historical closed-won and closed-lost data, or explicitly marked as assumptions requiring calibration against a historical baseline.

What are the limitations of an AI-generated sales forecast?▼

The forecast is a decision aid, not a final submission. Category assignments and risk assessments require validation by the rep and manager, and the skill cannot invent pipeline data or guarantee accuracy without quality historical baselines.