home / skills / onewave-ai / claude-skills / sales-forecast-builder

sales-forecast-builder skill

safe

This skill builds weighted sales forecasts with probability, tracks accuracy, and analyzes commit versus best-case scenarios for actionable pipeline insights.

npx playbooks add skill onewave-ai/claude-skills --skill sales-forecast-builder

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: sales-forecast-builder
description: Weighted pipeline forecast by probability. Historical accuracy tracking, commit vs best-case scenarios, deal slippage patterns.
---

# Sales Forecast Builder
Weighted pipeline forecast by probability. Historical accuracy tracking, commit vs best-case scenarios, deal slippage patterns.

## Instructions

You are an expert sales operations leader. Build accurate forecasts with multiple scenarios, track accuracy, and identify improvement opportunities.

### Output Format

```markdown
# Sales Forecast Builder Output

**Generated**: {timestamp}

---

## Results

[Your formatted output here]

---

## Recommendations

[Actionable next steps]

```

### Best Practices

1. **Be Specific**: Focus on concrete, actionable outputs
2. **Use Templates**: Provide copy-paste ready formats
3. **Include Examples**: Show real-world usage
4. **Add Context**: Explain why recommendations matter
5. **Stay Current**: Use latest best practices for sales-leadership

### Common Use Cases

**Trigger Phrases**:
- "Help me with [use case]"
- "Generate [output type]"
- "Create [deliverable]"

**Example Request**:
> "[Sample user request here]"

**Response Approach**:
1. Understand user's context and goals
2. Generate comprehensive output
3. Provide actionable recommendations
4. Include examples and templates
5. Suggest next steps

Remember: Focus on delivering value quickly and clearly!

Overview

This skill builds weighted pipeline forecasts using deal-stage probabilities and produces multiple scenarios (commit, best-case, and upside). It also tracks historical forecast accuracy and surfaces deal slippage and commit leakage patterns. The output is concise, actionable, and focused on next-step recommendations for sales leaders and operations teams.

How this skill works

I ingest opportunity records with stage, value, close date, and historical movement data, then apply stage probability weights to produce a probabilistic expected value per period. I generate parallel scenarios: commit (high-confidence), best-case (optimistic but plausible), and weighted-probability (statistical). I compare each forecast to historical outcomes to compute accuracy, bias, and slippage trends.

When to use it

Quarterly and monthly revenue planning
Weekly roll-ups for pipeline reviews and rep coaching
Validating commit numbers before executive forecasting
Identifying deals at risk due to repeated slippage
Measuring forecast accuracy over time to improve process

Best practices

Standardize stage definitions and probabilities before forecasting
Use rolling 3–6 month windows to compute historical accuracy and adjust probabilities
Segment forecasts by rep, product, and region to surface systemic patterns
Track commit vs best-case separately to hold sellers accountable and tune expectations
Automate data pulls and produce the same report cadence for comparability

Example use cases

Generate a 3-scenario forecast for next quarter with stage-weighted expected ARR and commit totals
Run historical accuracy report showing forecast error by stage and rep for the past 6 months
Produce a slippage dashboard that lists deals with repeated close-date moves and recommended interventions
Create weekly commit validation notes for the CRO highlighting high-risk $ deals and suggested actions
Output a one-page executive summary with variance to plan and recommended adjustments

FAQ

What inputs do I need to run this skill?

Opportunity ID, stage, value, expected close date, owner, and historical stage movement or close-date changes. More fields (product, ARR vs. one-time) improve segmentation.

How do you calculate stage probabilities?

Probabilities can be set via organization defaults or derived from historical conversion rates by stage and segment; I recommend using historical conversion with regular recalibration.