# pipeline-health-analyzer

Reads a deal export, flags stalled and at-risk opportunities, scores close probability and prescribes next actions.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 319 installs · ★ 382
- Author: OneWave-AI
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/OneWave-AI/claude-skills/tree/HEAD/pipeline-health-analyzer/SKILL.md
- Updated: 2026-10-20
- HTML: https://affpapa.org/en/nevibecode/skills/pipeline-health-analyzer

## What it does

The skill takes a CSV with deal name, stage, value, rep, deal age, days in current stage, last activity date, close date and probability. From that it computes pipeline-by-stage metrics: deal count, total value, average deal size, average days in stage and stage-to-stage conversion. Each number gets compared against historical benchmarks.

Every deal is scored across six health dimensions: stage velocity, engagement, qualification depth, stakeholder coverage, competitive position and 30-day momentum. Stalled and at-risk deals get flagged when they run past benchmark time in stage, go quiet, slip their close date or sit on a single contact. For critical deals the skill works out the root cause and ranks actions as immediate, this-week or backstop. Re-engagement copy comes from `references/email-templates.md`.

The forecast splits deals into Commit, Best Case, Pipeline and Upside, with weighted and risk-adjusted value, then models best-case, expected and worst-case against quota. The report is assembled from `references/output-template.md` using plain status words (Healthy / At Risk / Critical) and trend words (Up / Flat / Down). The author advises running it weekly rather than monthly, because pipeline health degrades fast. No export means the first step is a request for one.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill pipeline-health-analyzer -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/pipeline-health-analyzer/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/pipeline-health-analyzer/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill pipeline-health-analyzer -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/pipeline-health-analyzer/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill pipeline-health-analyzer -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/pipeline-health-analyzer/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install OneWave-AI/claude-skills pipeline-health-analyzer
```

2. **Manually: put the files in place**
- File: `.github/skills/pipeline-health-analyzer/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill pipeline-health-analyzer -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/OneWave-AI/claude-skills --path pipeline-health-analyzer
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/pipeline-health-analyzer/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add OneWave-AI/claude-skills --skill pipeline-health-analyzer -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/OneWave-AI/claude-skills/tree/HEAD/pipeline-health-analyzer/SKILL.md

## When to use it
- spot deals sitting in one stage past the benchmark
- compute stage-to-stage conversion and compare it to historical rates
- score close probability per deal and re-score the outliers
- build a forecast split into Commit, Best Case, Pipeline and Upside
- draft re-engagement emails for deals that have gone dark

## Example prompts
- Here's the quarterly deal export — analyze pipeline health and show me the stalled deals
- Score close probability for every deal and re-score the ones that break calibration
- Build the quarterly forecast by category and model best-case, expected and worst-case

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 47452e98ae99fbb7.

BEGIN THIRD-PARTY FILE 47452e98ae99fbb7
```text
---
name: pipeline-health-analyzer
description: Analyze pipeline health, identify stalled deals, predict close probability, and suggest actions to move deals forward. Improves forecast accuracy and prevents revenue leakage. Use when deals get stuck or forecast accuracy is poor.
---

# Pipeline Health Analyzer

Identify pipeline risks, predict deal outcomes, and prescribe specific actions to accelerate stalled opportunities.

## Contents

- references/output-template.md - Full report skeleton to populate.
- references/stage-analysis.md - Per-stage deep-dive patterns (Discovery, Demo, Proposal) and why deals stall.
- references/forecasting.md - Forecast tables, probability calibration, AI re-scoring, scenario planning.
- references/email-templates.md - Re-engagement email templates for stalled and dark deals.
- references/examples.md - Trigger phrases, a worked example request, and best practices.

## Workflow

1. Obtain the pipeline data. Request a CSV export with deal name, stage, value, rep, deal age, days in current stage, last activity date, close date, and probability if not provided.
2. Compute pipeline-by-stage metrics: deal count, total value, average deal size, average days in stage, and stage-to-stage conversion rates. Compare each against historical benchmarks.
3. Score each deal across six health dimensions: stage velocity, engagement level, qualification depth, stakeholder coverage, competitive position, and 30-day momentum.
4. Identify stalled and at-risk deals. Flag deals exceeding benchmark time in stage, with no recent activity, with slipped close dates, or with single-threaded contacts.
5. For each critical deal, determine the root cause and prescribe prioritized actions (immediate, this-week, backstop). Pull re-engagement copy from references/email-templates.md.
6. Build the forecast: categorize deals (Commit/Best Case/Pipeline/Upside), calculate weighted and risk-adjusted value, check probability calibration against actual close rates, and re-score outliers. Follow references/forecasting.md.
7. Model best-case, expected, and worst-case scenarios against quota, with a mitigation plan for the downside.
```
END THIRD-PARTY FILE 47452e98ae99fbb7

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