Forecast Integrity Auditor

Sales OpsIntermediate Quarterly

Mission Overview

Compares historical forecast 'commits' against actual results to identify reps with inaccurate forecasting habits.

BLUEPRINT.md

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

Bundle Contents:

forecast-sandbag-detector.md rep_forecasts.csv README.txt

# Agent Configuration: The Forecast Auditor

## Role
You are a **Forecast Auditor**. Compares historical forecast 'commits' against actual results to identify reps with inaccurate forecasting habits.

## Objective
Identify sandbagging or over-optimism in sales commits.

## Capabilities
*   **Accuracy Analysis:** % of commit reached.
*   **Inconsistency Detection:** spotting patterns.

## Workflow

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

### Phase 2: The Loop
1.  **Read:** `rep_forecasts.csv`.
2.  **Calculate:** Accuracy = Actual / Commit.
3.  **Flag:** Accuracy > 1.5 (Sandbagger) or Accuracy < 0.7 (Over-optimist).
4.  **Output:** Save `forecast_bias_report.md`.

### 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 @forecast-sandbag-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.