What data does the Pipeline skill need to work?

The skill requires deal names, values, current stages, last activity dates, expected close dates, and assigned reps, which can be provided via CRM MCP or data exports.

How does it calculate weighted pipeline?

It multiplies each deal's value by its stage probability (e.g., Discovery at 20%, Proposal at 60%) to provide a realistic revenue forecast rather than just a total sum.

Can this skill help with pipeline gaps?

Yes, it calculates the coverage ratio relative to remaining quota and recommends specific lead sources or channels to prioritize based on historical win rates.

What triggers a deal to be flagged as 'At Risk'?

Deals are flagged if they show no activity for over 14 days, exceed the average duration for their current stage, or if the expected close date passes without updates.

Sales Pipeline Analyzer

Name: Sales Pipeline Analyzer
Author: panaversity

bypanaversity

•

マーケティングオートメーション

Performs deep-dive sales pipeline analysis and revenue forecasting to identify at-risk deals and optimize funnel health.

The Pipeline skill provides AI agents with a specialized framework for auditing CRM data and generating actionable sales intelligence. It goes beyond simple reporting by calculating weighted forecasts, identifying stalled deals based on activity signals, and measuring pipeline coverage ratios against quotas. This skill is essential for RevOps and sales leadership to maintain pipeline hygiene, detect sandbagging or over-optimism, and determine exactly where to focus effort to meet quarterly revenue targets.

主な機能

01Pipeline coverage and velocity metric calculation

02Weighted pipeline and revenue forecast modeling

0311 GitHub stars

04Win rate and deal size analysis by lead source

05Automated risk detection for neglected or stalled deals

06Standardized QBR and weekly pipeline report generation

ユースケース

01Conducting automated weekly pipeline reviews to identify deals requiring immediate intervention

02Benchmarking rep-submitted forecasts against historical win rates and engagement signals

03Analyzing lead source performance to optimize marketing spend and fill pipeline gaps

主な機能

01Pipeline coverage and velocity metric calculation

02Weighted pipeline and revenue forecast modeling

0311 GitHub stars

04Win rate and deal size analysis by lead source

05Automated risk detection for neglected or stalled deals

06Standardized QBR and weekly pipeline report generation

ユースケース

01Conducting automated weekly pipeline reviews to identify deals requiring immediate intervention

02Benchmarking rep-submitted forecasts against historical win rates and engagement signals

03Analyzing lead source performance to optimize marketing spend and fill pipeline gaps