
Pipeline Forecasting
- 161 installs
- 145 repo stars
- Updated April 2, 2026
- guia-matthieu/clawfu-skills
Forecast revenue from CRM pipeline stages, conversion assumptions, and deal timing to plan hiring, spend, and quarterly targets.
About
Builds sales pipeline forecasts from stage definitions, historical conversion, average deal size, and close timing so operators estimate revenue ranges, quota coverage, and resource needs during growth.
- Stage-weighted revenue projections
- Scenario modeling for conversion changes
- Surfaces coverage and quota gaps
- Incorporates deal age and slippage risk
- Supports board-ready forecast narratives
Pipeline Forecasting by the numbers
- 161 all-time installs (skills.sh)
- Ranked #378 of 853 Sales & Marketing skills by installs in the Skillselion catalog
- Data as of Aug 3, 2026 (Skillselion catalog sync)
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| Installs | 161 |
|---|---|
| repo stars | ★ 145 |
| Last updated | April 2, 2026 |
| Repository | guia-matthieu/clawfu-skills ↗ |
What it does
Forecast revenue from CRM pipeline stages, conversion assumptions, and deal timing to plan hiring, spend, and quarterly targets.
Files
Pipeline Forecasting
Build accurate, data-driven revenue forecasts using historical conversion rates, deal velocity, and confidence-weighted projections.
When to Use This Skill
- Weekly/monthly pipeline reviews with leadership
- Board meeting revenue projections
- Quota setting and territory planning
- Identifying gaps between forecast and target
- Scenario planning for best/worst/likely outcomes
Methodology Foundation
Based on Clari's Revenue Operations methodology and Forrester's B2B Revenue Waterfall, combining:
- Weighted pipeline (probability × value)
- Historical stage conversion rates
- Deal velocity analysis
- Commit vs. upside categorization
What Claude Does vs What You Decide
| Claude Does | You Decide |
|---|---|
| Calculates weighted pipeline by stage | Which deals to include/exclude |
| Applies historical conversion rates | Override factors for specific deals |
| Generates confidence intervals | Final commit number to leadership |
| Identifies forecast risks | Actions to close gaps |
| Models best/worst/likely scenarios | Which scenario to plan against |
What This Skill Does
1. Ingests pipeline data - Current opportunities with stage, value, close date 2. Applies conversion math - Historical win rates by stage, segment, rep 3. Calculates weighted forecast - Probability-adjusted revenue projection 4. Generates scenarios - Best case, commit, worst case with confidence bands 5. Identifies risks - Deals pushing, pipeline gaps, coverage ratios
How to Use
I need a pipeline forecast for Q1. Here's our current pipeline:
[Paste pipeline data: Deal name, Stage, Value, Close Date, Rep]
Historical context:
- Average win rate: 25%
- Stage 3→Close rate: 45%
- Stage 4→Close rate: 70%
- Average sales cycle: 45 days
Target: $2.5M for Q1Instructions
Step 1: Pipeline Categorization
Segment deals into:
- Commit - High confidence (Stage 4+, verbal commit)
- Best Case - Medium confidence (Stage 3, engaged)
- Upside - Low confidence (Stage 1-2, early)
Step 2: Weighted Calculation
Weighted Value = Deal Value × Stage Probability × Rep Factor
Stage Probabilities (adjust to your data):
- Stage 1 (Discovery): 10%
- Stage 2 (Qualification): 20%
- Stage 3 (Proposal): 40%
- Stage 4 (Negotiation): 70%
- Stage 5 (Verbal): 90%Step 3: Coverage Analysis
Pipeline Coverage = Total Pipeline ÷ Target
- Healthy: 3-4x coverage
- At Risk: 2-3x coverage
- Critical: <2x coverageStep 4: Scenario Modeling
- Worst Case: Commit only (90%+ probability deals)
- Likely Case: Commit + 50% of Best Case
- Best Case: Commit + Best Case + 25% of Upside
Step 5: Risk Identification
Flag deals with:
- Close date in past (needs update)
- No activity in 14+ days (stalled)
- Pushing from previous quarter (slip risk)
- Missing key fields (data quality)
Examples
Example 1: Quarterly Forecast
Input:
Pipeline for Q1:
- Acme Corp: Stage 4, $150K, Jan 30, Rep: Sarah
- Beta Inc: Stage 3, $80K, Feb 15, Rep: Mike
- Gamma Ltd: Stage 2, $200K, Mar 1, Rep: Sarah
- Delta Co: Stage 4, $120K, Feb 28, Rep: Lisa
- Echo Systems: Stage 1, $500K, Mar 15, Rep: Mike
Target: $400KOutput:
## Q1 Pipeline Forecast
### Weighted Pipeline
| Deal | Stage | Value | Probability | Weighted |
|------|-------|-------|-------------|----------|
| Acme Corp | 4 | $150K | 70% | $105K |
| Delta Co | 4 | $120K | 70% | $84K |
| Beta Inc | 3 | $80K | 40% | $32K |
| Gamma Ltd | 2 | $200K | 20% | $40K |
| Echo Systems | 1 | $500K | 10% | $50K |
| **Total** | | **$1.05M** | | **$311K** |
### Scenario Analysis
- **Worst Case (Commit)**: $189K (Acme + Delta at 70%)
- **Likely Case**: $245K (Commit + 50% Best Case)
- **Best Case**: $361K (All weighted)
### Coverage Ratio
- Pipeline: $1.05M
- Target: $400K
- Coverage: 2.6x ⚠️ (At Risk - need 3x+)
### Risks Identified
1. ⚠️ Echo Systems ($500K) is Stage 1 with Mar close - unlikely
2. ⚠️ Gap of $155K between Likely ($245K) and Target ($400K)
### Recommendations
1. Pressure test Acme and Delta for verbal commits
2. Accelerate Beta Inc to Stage 4 this week
3. Add $200K+ new pipeline to reach 3x coverageExample 2: Monthly Roll-Up
Input:
January pipeline by rep:
Sarah: $450K weighted, $280K commit
Mike: $320K weighted, $150K commit
Lisa: $280K weighted, $200K commit
Team target: $500KOutput:
## January Team Forecast
| Rep | Weighted | Commit | % to Target |
|-----|----------|--------|-------------|
| Sarah | $450K | $280K | 56% |
| Mike | $320K | $150K | 30% |
| Lisa | $280K | $200K | 40% |
| **Team** | **$1.05M** | **$630K** | **126%** |
### Forecast Summary
- **Team Commit**: $630K (126% of $500K target) ✅
- **Confidence**: HIGH - commit exceeds target
### Risk Watch
- Mike at 30% commit coverage - needs deal acceleration
- Sarah carrying most weight - concentration riskSkill Boundaries
What This Skill Does Well
- Mathematical pipeline calculations
- Scenario modeling with clear assumptions
- Identifying data quality issues
- Coverage ratio analysis
What This Skill Cannot Do
- Predict which specific deals will close (human judgment)
- Account for market changes or competitive moves
- Replace rep-level deal knowledge
- Guarantee forecast accuracy
When to Escalate to Human
- Deals with unusual circumstances (M&A, champion left)
- Market disruptions affecting close rates
- Strategic accounts requiring executive judgment
- Final commit numbers for board/investors
Iteration Guide
Follow-up Prompts
- "What if we lose the top 2 deals? Show me that scenario."
- "Apply a 20% haircut to all Stage 2 deals and recalculate."
- "Which deals have the highest impact on our forecast?"
- "Show me the gap between forecast and target by month."
Refinement Cycle
1. Generate initial forecast → Review with reps 2. Update deal probabilities based on rep input 3. Re-run forecast with adjusted assumptions 4. Lock commit number, track weekly variance
Checklists & Templates
Weekly Forecast Review Checklist
- [ ] All deals have current close dates
- [ ] Stage progression updated this week
- [ ] Commit deals have next steps scheduled
- [ ] Risks flagged and mitigation assigned
- [ ] Coverage ratio calculated
Forecast Template
## [Period] Revenue Forecast
**Generated:** [Date]
**Pipeline Cutoff:** [Date]
### Summary
- Target: $X
- Commit: $X (X% of target)
- Best Case: $X
- Coverage: Xx
### By Segment
[Table]
### Risks & Mitigations
[List]
### Actions This Week
[List]References
- Clari Revenue Operations Playbook
- Forrester B2B Revenue Waterfall Model
- MEDDICC Deal Qualification Framework
- Gartner Sales Forecasting Best Practices
Related Skills
deal-risk-scoring- Assess individual deal healthlead-scoring- Qualify top-of-funnelaccount-health- Customer retention signals
Skill Metadata
- Domain: RevOps
- Complexity: Intermediate
- Mode: centaur
- Time to Value: 15-30 minutes per forecast
- Prerequisites: Pipeline data export, historical win rates