Revenue Forecasting Engine

Generates detailed revenue forecasts using pipeline weighting, cohort analysis, scenario modeling, seasonality, and leading indicators to inform business dec...

Security Scan

The skill is an instruction-only revenue-forecasting template that asks for user-provided business metrics and does not request credentials or install artifacts — its requirements and instructions are consistent with its stated purpose.

Purpose & Capability

Name/description (revenue forecasting) align with the content of SKILL.md and README: models, cohort analysis, seasonality, and leading indicators. There are no unrelated binaries, environment variables, or config paths requested.

Instruction Scope

SKILL.md stays within forecasting scope and explicitly asks for specific business metrics. One ambiguous phrase — 'Ask for (or use available data)' — could grant broad discretion about what data sources to access; the skill does not specify where 'available data' may come from (local files, CRM, spreadsheets, or agent context). Recommend confirming data sources before providing access to sensitive systems.

Install Mechanism

This is instruction-only (no install spec, no code written to disk) which is low-risk. README shows a 'clawhub install' example but the registry contains no install manifest; if you plan to run any install command, verify the publisher/source first.

Credentials

The skill requests no environment variables, credentials, or config paths — appropriate for a template that operates on user-supplied metrics. No disproportionate secret access is requested.

Persistence & Privilege

always is false and the skill does not request system-wide changes or persistent presence. It does not modify other skills or agent configs based on the provided materials.

Assessment

This skill is an instruction-only forecasting template and appears coherent with its stated purpose: it will ask you for business metrics and produce forecasts based on those inputs. It does not request credentials or install code. Before using it, verify where the agent will read data from when it says 'use available data' — avoid pointing it at sensitive production systems or sharing credentials. If you see a prompt to run 'clawhub install' or similar, confirm the install source and publisher (the registry metadata lists an owner id but no homepage). Test the skill with dummy or non-sensitive data first, and review outputs for accuracy before acting on them.

Build accurate, data-driven revenue forecasts your board and investors actually trust.

What This Does

Generates a complete revenue forecasting model covering:

1. Pipeline-Weighted Forecast — Apply stage-specific close rates to your current pipeline
2. Cohort Analysis — Track revenue by customer cohort with expansion/contraction/churn
3. Scenario Modeling — Bear/base/bull projections with probability weighting
4. Seasonality Adjustments — Monthly coefficients based on your historical patterns
5. Leading Indicators — Track signals that predict revenue 60-90 days out

Instructions

When the user asks for a revenue forecast, follow this framework:

Step 1: Gather Inputs

Ask for (or use available data):

• Current MRR/ARR
• Pipeline by stage with deal values
• Historical close rates by stage
• Average sales cycle length
• Net revenue retention rate
• Expansion revenue %

Step 2: Build the Pipeline Forecast

Stage-Weighted Model:

Stage	Probability	Weighted Value
Discovery	10%	Deal × 0.10
Demo/Eval	25%	Deal × 0.25
Proposal Sent	50%	Deal × 0.50
Negotiation	75%	Deal × 0.75
Verbal Commit	90%	Deal × 0.90
Closed Won	100%	Deal × 1.00

Adjustment factors:

• Deal age penalty: -5% per month past avg cycle
• Champion risk: -20% if no identified champion
• Budget confirmed: +10% if budget is allocated
• Competitive deal: -15% if competitor identified

Step 3: Cohort Revenue Model

Track each monthly cohort:

Month 0: New MRR from cohort
Month 1: Retained MRR × (1 - monthly churn rate)
Month 3: Add expansion revenue (avg 2-5% monthly for healthy SaaS)
Month 6: Steady-state retention rate applies
Month 12: Mature cohort — use net revenue retention

Benchmarks by company stage:

Metric	Seed	Series A	Series B+
Gross Churn	3-5%/mo	2-3%/mo	1-2%/mo
Net Retention	90-100%	100-110%	110-130%
Expansion %	5-10%	10-20%	20-40%
CAC Payback	18-24 mo	12-18 mo	6-12 mo

Step 4: Scenario Analysis

Bear Case (20% probability):

• Pipeline closes at 60% of weighted value
• Churn increases 50%
• No expansion revenue
• 1 key deal slips each quarter

Base Case (60% probability):

• Pipeline closes at weighted value
• Current retention rates hold
• Historical expansion rate
• Normal seasonality

Bull Case (20% probability):

• Pipeline closes at 120% of weighted value
• Retention improves 10%
• Expansion accelerates 25%
• 1 surprise large deal per quarter

Expected Value = (Bear × 0.2) + (Base × 0.6) + (Bull × 0.2)

Step 5: Seasonality Coefficients

Apply monthly adjustment factors:

Month	B2B SaaS	Ecommerce	Professional Services
Jan	0.85	0.70	0.90
Feb	0.90	0.75	0.95
Mar	1.05	0.85	1.10
Apr	1.00	0.90	1.00
May	0.95	0.90	0.95
Jun	1.10	0.95	1.05
Jul	0.85	0.85	0.85
Aug	0.80	0.90	0.80
Sep	1.10	1.00	1.10
Oct	1.05	1.05	1.05
Nov	1.15	1.40	1.10
Dec	1.20	1.75	1.15

Step 6: Leading Indicators Dashboard

Track these weekly — they predict revenue 60-90 days out:

Indicator	Weight	Signal
Qualified pipeline created	25%	New opps entering Stage 2+
Demo-to-proposal rate	20%	Conversion velocity
Average deal size trend	15%	Moving up or down?
Sales cycle length	15%	Getting longer = red flag
Inbound lead volume	10%	Marketing effectiveness
Website trial signups	10%	Self-serve demand
Customer NPS/CSAT	5%	Retention predictor

Step 7: Output Format

Present the forecast as:

REVENUE FORECAST — [Period]
================================
Current ARR: $X
Pipeline (Weighted): $X
Expected New ARR: $X

12-Month Projection:
  Bear:  $X (20%)
  Base:  $X (60%)
  Bull:  $X (20%)
  Expected: $X

Key Risks:
  1. [Risk] — [Mitigation]
  2. [Risk] — [Mitigation]

Leading Indicators:
  🟢 [Healthy metric]
  🟡 [Watch metric]
  🔴 [Concerning metric]

Next Month Actions:
  1. [Specific action]
  2. [Specific action]

Red Flags to Call Out

• Pipeline coverage < 3x target = high risk

• 40% of forecast from 1-2 deals = concentration risk

• Average deal age exceeding 1.5x normal cycle = stalling
• Declining demo-to-close rate = product-market fit erosion
• Rising CAC payback period = unit economics degrading

Revenue Recognition Notes

• SaaS: Recognize ratably over contract term
• Services: Recognize on delivery/milestones
• Usage-based: Recognize on consumption
• Annual prepay: Deferred revenue, recognize monthly

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