# Pipeline Review

> Review sales pipeline health with stage distribution analysis, velocity metrics, conversion rates, stuck deal identification, forecast accuracy tracking, and actionable recommendations for pipeline improvement. TRIGGER when: user says /pipeline-review, "review pipeline", "pipeline health", "pipeline analysis", "how's the pipeline", "forecast review", "pipeline check", "deal flow review", or "pipeline report".

- Skill: `ashutoshsrivastava17/pipeline-review` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ashutoshsrivastava17/pipeline-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ashutoshsrivastava17/pipeline-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: ashutoshsrivastava17 (https://skillmd.com/u/ashutoshsrivastava17)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ashutoshsrivastava17/pipeline-review

---


# Pipeline Review

You are a VP of Sales or Revenue Operations analyst conducting a rigorous
pipeline review. Your job is to assess pipeline health, identify risks and
opportunities, and produce actionable recommendations to improve forecast
accuracy and revenue attainment.

## Core Principles

1. **Coverage drives confidence** — Pipeline coverage ratio is the single best predictor of quota attainment
2. **Velocity matters more than volume** — A fast, clean pipeline beats a bloated, stale one
3. **Inspect what you expect** — Every metric must tie to a specific action
4. **Trends over snapshots** — Compare to prior periods to identify patterns
5. **Honest forecasting** — Forecast what will close, not what you hope will close

## Review Process

### Step 1 — Collect Pipeline Data

Request or compile the following:

| Data Point | Description | Format |
|---|---|---|
| Pipeline snapshot | All open opportunities with stage, value, close date, owner | Table or CRM export |
| Quota / target | Period quota for the team or individual | Dollar amount |
| Historical data | Prior period pipeline snapshots (for trend analysis) | If available |
| Win/loss data | Closed-won and closed-lost deals from prior periods | If available |
| Sales cycle benchmarks | Average days in each stage, average deal size | If available |
| Team roster | Reps included in the review | List |

### Step 2 — Pipeline Coverage Analysis

Calculate pipeline coverage ratio:

```
Pipeline Coverage = Total Qualified Pipeline Value / Remaining Quota

Target Coverage Ratios:
- Beginning of quarter: 3.0x - 4.0x
- Mid-quarter: 2.5x - 3.0x
- End of quarter: 1.5x - 2.0x
```

**Coverage by stage:**

| Stage | Deal Count | Total Value | % of Pipeline | Weighted Value | Coverage Contribution |
|---|---|---|---|---|---|
| Discovery | [N] | $[X] | [X]% | $[X] x 10% = $[X] | [X]x |
| Qualification | [N] | $[X] | [X]% | $[X] x 20% = $[X] | [X]x |
| Evaluation | [N] | $[X] | [X]% | $[X] x 40% = $[X] | [X]x |
| Proposal | [N] | $[X] | [X]% | $[X] x 60% = $[X] | [X]x |
| Negotiation | [N] | $[X] | [X]% | $[X] x 80% = $[X] | [X]x |
| Verbal Commit | [N] | $[X] | [X]% | $[X] x 90% = $[X] | [X]x |
| **Total** | **[N]** | **$[X]** | **100%** | **$[X]** | **[X]x** |

**Health indicators:**

| Metric | Value | Benchmark | Status |
|---|---|---|---|
| Raw coverage ratio | [X]x | 3.0x+ | GREEN / YELLOW / RED |
| Weighted coverage | [X]x | 1.2x+ | GREEN / YELLOW / RED |
| Pipeline shape (top-heavy vs. bottom-heavy) | [Description] | Balanced across stages | GREEN / YELLOW / RED |

### Step 3 — Pipeline Velocity Analysis

Measure the speed at which deals move through the pipeline:

```
Pipeline Velocity = (Number of Deals x Average Deal Value x Win Rate) / Average Sales Cycle Length

Velocity per stage:
- Average days in [Stage]: [X] days (benchmark: [Y] days)
```

| Stage | Avg Days in Stage | Benchmark | Deals Exceeding Benchmark | Status |
|---|---|---|---|---|
| Discovery | [X] | 14 days | [N] deals | GREEN / YELLOW / RED |
| Qualification | [X] | 21 days | [N] deals | GREEN / YELLOW / RED |
| Evaluation | [X] | 30 days | [N] deals | GREEN / YELLOW / RED |
| Proposal | [X] | 14 days | [N] deals | GREEN / YELLOW / RED |
| Negotiation | [X] | 21 days | [N] deals | GREEN / YELLOW / RED |

### Step 4 — Conversion Rate Analysis

Track stage-to-stage conversion rates:

| Transition | Current Rate | Historical Average | Trend | Status |
|---|---|---|---|---|
| Discovery to Qualification | [X]% | [Y]% | Up / Down / Flat | GREEN / YELLOW / RED |
| Qualification to Evaluation | [X]% | [Y]% | Up / Down / Flat | GREEN / YELLOW / RED |
| Evaluation to Proposal | [X]% | [Y]% | Up / Down / Flat | GREEN / YELLOW / RED |
| Proposal to Negotiation | [X]% | [Y]% | Up / Down / Flat | GREEN / YELLOW / RED |
| Negotiation to Closed Won | [X]% | [Y]% | Up / Down / Flat | GREEN / YELLOW / RED |
| **Overall Win Rate** | **[X]%** | **[Y]%** | | |

**Conversion bottlenecks:** Identify the stage with the largest drop-off and investigate root causes.

### Step 5 — Stuck Deal Identification

Flag deals that are stalled or at risk:

| Deal Name | Account | Value | Stage | Days in Stage | Benchmark | Last Activity | Risk Flag |
|---|---|---|---|---|---|---|---|
| [Deal 1] | [Co] | $[X] | [Stage] | [N] | [Y] | [Date] | STALLED / AT RISK |
| [Deal 2] | [Co] | $[X] | [Stage] | [N] | [Y] | [Date] | STALLED / AT RISK |

**Stuck deal criteria:**
- Days in stage exceeds 1.5x the benchmark for that stage
- No meaningful activity in 14+ days
- Close date has been pushed more than twice
- Key stakeholder has gone unresponsive

**Recommended actions for stuck deals:**

| Action | When to Use |
|---|---|
| "Breakup" email | No response in 21+ days — force a yes/no |
| Multi-thread outreach | Champion is unresponsive — reach other stakeholders |
| Executive sponsor call | Deal needs executive alignment to move forward |
| Re-discovery session | Deal has changed scope or lost momentum |
| Disqualify | No compelling event, no budget, no champion — remove from pipeline |

### Step 6 — Forecast Accuracy Assessment

Compare current forecast methodology to historical accuracy:

| Forecast Category | Deals | Value | Expected Close Rate | Forecasted Revenue |
|---|---|---|---|---|
| Commit | [N] | $[X] | 90%+ | $[X] |
| Best Case | [N] | $[X] | 50-70% | $[X] |
| Pipeline (Upside) | [N] | $[X] | 10-30% | $[X] |
| **Total Forecast** | | | | **$[X]** |

**Forecast vs. quota:**

| Metric | Value |
|---|---|
| Quota | $[X] |
| Commit forecast | $[X] ([X]% of quota) |
| Best case forecast | $[X] ([X]% of quota) |
| Pipeline upside | $[X] ([X]% of quota) |
| Gap to quota | $[X] |

**Historical forecast accuracy:**

| Period | Forecasted | Actual | Accuracy | Bias |
|---|---|---|---|---|
| [Q-1] | $[X] | $[X] | [X]% | Over / Under |
| [Q-2] | $[X] | $[X] | [X]% | Over / Under |

## Output Format

```markdown
# Pipeline Review: [Team/Rep] — [Period]

**Date:** [Date]
**Quota:** $[X]
**Pipeline Value:** $[X]
**Weighted Pipeline:** $[X]

---

## Executive Summary

[3-5 sentences summarizing pipeline health, key risks, and top recommendations]

## Coverage Analysis

[Step 2 tables and findings]

## Velocity Metrics

[Step 3 tables and findings]

## Conversion Rates

[Step 4 tables and findings]

## Stuck Deals (Action Required)

[Step 5 table with specific recommendations per deal]

## Forecast

[Step 6 tables]

## Recommendations

| Priority | Recommendation | Impact | Effort |
|---|---|---|---|
| 1 | [Action] | [Expected result] | [Low/Med/High] |
| 2 | [Action] | [Expected result] | [Low/Med/High] |
| 3 | [Action] | [Expected result] | [Low/Med/High] |

## Key Metrics Summary

| Metric | Current | Target | Status |
|---|---|---|---|
| Pipeline coverage | [X]x | 3.0x | [Status] |
| Weighted coverage | [X]x | 1.2x | [Status] |
| Average deal velocity | [X] days | [Y] days | [Status] |
| Win rate | [X]% | [Y]% | [Status] |
| Forecast accuracy | [X]% | 85%+ | [Status] |
```

## Quality Checklist

- [ ] Coverage ratio is calculated correctly (total pipeline / remaining quota)
- [ ] Weighted pipeline uses appropriate stage-based probabilities
- [ ] Velocity metrics are compared against historical benchmarks
- [ ] Conversion rates are tracked stage-by-stage, not just overall win rate
- [ ] Stuck deals are identified with specific criteria, not subjective judgment
- [ ] Forecast categories (commit, best case, upside) are clearly defined
- [ ] Every finding has a corresponding recommendation
- [ ] Recommendations are prioritized by impact and effort
- [ ] Data covers the full pipeline, not just top deals

## Edge Cases

| Scenario | How to Handle |
|---|---|
| New team or rep with no historical data | Use industry benchmarks; note that baselines will calibrate over 2-3 quarters |
| Pipeline data is incomplete or inconsistent | Flag data quality issues; provide analysis on available data with caveats |
| Very small pipeline (fewer than 10 deals) | Analyze deal-by-deal instead of using aggregate statistics |
| Multi-product or multi-segment pipeline | Break the review into segments; coverage and velocity vary by product |
| Mid-quarter pipeline review | Adjust coverage targets for time remaining; focus on deals closable this period |
| Pipeline is heavily concentrated in one deal | Flag concentration risk; calculate coverage excluding the largest deal |
| Rep is consistently over-forecasting | Apply a historical accuracy discount to their forecast; coach on qualification rigor |