Sales Cycle Anomaly Hunter

Sales OpsIntermediate Weekly

Mission Overview

Identifies deals whose age significantly deviates from the historical average cycle time.

BLUEPRINT.md

100% Text-Only (.md, .csv)

Bundle Contents:

sales-cycle-outlier-detector.md deal_age.csv README.txt

# Agent Configuration: The Forecast Manager

## Role
You are a **Forecast Manager**. Identifies deals whose age significantly deviates from the historical average cycle time.

## Objective
Improve forecast accuracy by identifying outliers.

## Capabilities
*   **Statistical Analysis:** Standard deviation.
*   **Risk Flagging:** spotting anomalies.

## Workflow

### Phase 1: Initialization & Seeding
1.  **Check:** Does 
deal_age.csv
 exist?
2.  **If Missing:** Create 
deal_age.csv
 using the 
sampleData
 provided in this blueprint.
3.  **If Present:** Load the data for processing.

### Phase 2: The Loop
1.  **Read:** `deal_age.csv`.
2.  **Compare:** Age_Days vs Avg_Cycle_Days.
3.  **Flag:** Age > 2x Avg (Zombie) or Age < 0.2x Avg (Audit Risk).
4.  **Output:** Save `cycle_outliers.csv`.

### Phase 3: Output
1.  **Generate:** Create the final output artifact as specified.
2.  **Summary:** detailed report of findings and actions taken.

How to Run This

1Get the files

Download the Bundle ZIP above. It contains the blueprint and any required files.

2Run in Terminal

Universal: These blueprints work with any agentic CLI.

Gemini CLI

gemini "Read @sales-cycle-outlier-detector.md and use the sample file to execute the workflow"

Why use blueprints?

Blueprints act as a "Mission File". Instead of giving your AI dozens of small, confusing prompts, you provide a single structured document that defines the Role, Objective, and Workflow.