---
name: pipeline-hygiene-review
description: Audits a sales pipeline for stale activity, invalid dates, missing stage evidence, incomplete fields, unusual amounts, and unsupported forecast positions. Use for pipeline hygiene, CRM cleanup, forecast preparation, sales operations review, or manager briefing.
license: Apache-2.0
metadata:
  adlass.categories: "sales-crm/pipeline"
  adlass.industries: ""
  adlass.tags: "pipeline-hygiene, crm, forecast, data-quality, sales-operations, deals"
  adlass.adaptation: "mapping"
  adlass.source: "original"
  adlass.version: "1"
---

# Pipeline hygiene review

## Purpose

Find data and process conditions that make a pipeline unreliable, then provide evidence-backed correction suggestions. The review leaves source records untouched and distinguishes policy violations from informational anomalies.

## Scope

Open opportunities, stages, dates, amounts, owners, activity history, required fields, stage exit evidence, forecast categories, and prior pipeline snapshots.

**Excluded:** editing CRM records, deleting deals, notifying owners, and inventing missing activity.

## Data basis

- Current pipeline table and activity records.
- Stage definitions, hygiene rules, and required-field schema.
- Prior snapshot when available.

## Result

A row-level findings sheet and a manager briefing with counts, trends, and correction priorities.

## Quality criteria

- Every finding cites the rule and source row.
- Findings are reproducible from visible data.
- Stale, invalid, missing, and unusual conditions are distinct.
- Snapshot comparisons identify additions, removals, and changes.
- Findings do not imply deal quality beyond the rule tested.

## Instructions

Apply configured thresholds exactly; never invent a day count or amount tolerance. Check stage evidence before judging forecast. Use prior snapshots only for comparable periods. Group related findings per deal while retaining one row per issue. Include clean records in the population count.

## Adapt before use
Keep a clear audit trail throughout the run. Use the source wording and field values that support each material conclusion, and retain uncertainty when the evidence does not decide the issue. Separate a missing record from a negative result and a contradictory record from an exception. Prefer a short, prioritised result over unsupported completeness. Check that counts, identifiers, dates, and labels agree across the written result and the structured output. When a rule, source, or mapping is unavailable, name the limitation and explain how it affects interpretation. Do not silently infer ownership, approval, timing, or business intent.

- Add stage definitions, hygiene rules, required fields, and thresholds.
- Map pipeline and activity columns.
- Define the preferred severity and correction vocabulary.