# Send Revenue Forecasting Engine to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
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      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/afrexai-revenue-forecasting"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/afrexai-revenue-forecasting",
    "downloadUrl": "https://openagent3.xyz/downloads/afrexai-revenue-forecasting",
    "agentUrl": "https://openagent3.xyz/skills/afrexai-revenue-forecasting/agent",
    "manifestUrl": "https://openagent3.xyz/skills/afrexai-revenue-forecasting/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/afrexai-revenue-forecasting/agent.md"
  }
}
```
## Documentation

### Revenue Forecasting Engine

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

### What This Does

Generates a complete revenue forecasting model covering:

Pipeline-Weighted Forecast — Apply stage-specific close rates to your current pipeline
Cohort Analysis — Track revenue by customer cohort with expansion/contraction/churn
Scenario Modeling — Bear/base/bull projections with probability weighting
Seasonality Adjustments — Monthly coefficients based on your historical patterns
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:

StageProbabilityWeighted ValueDiscovery10%Deal × 0.10Demo/Eval25%Deal × 0.25Proposal Sent50%Deal × 0.50Negotiation75%Deal × 0.75Verbal Commit90%Deal × 0.90Closed Won100%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:

MetricSeedSeries ASeries B+Gross Churn3-5%/mo2-3%/mo1-2%/moNet Retention90-100%100-110%110-130%Expansion %5-10%10-20%20-40%CAC Payback18-24 mo12-18 mo6-12 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:

MonthB2B SaaSEcommerceProfessional ServicesJan0.850.700.90Feb0.900.750.95Mar1.050.851.10Apr1.000.901.00May0.950.900.95Jun1.100.951.05Jul0.850.850.85Aug0.800.900.80Sep1.101.001.10Oct1.051.051.05Nov1.151.401.10Dec1.201.751.15

### Step 6: Leading Indicators Dashboard

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

IndicatorWeightSignalQualified pipeline created25%New opps entering Stage 2+Demo-to-proposal rate20%Conversion velocityAverage deal size trend15%Moving up or down?Sales cycle length15%Getting longer = red flagInbound lead volume10%Marketing effectivenessWebsite trial signups10%Self-serve demandCustomer NPS/CSAT5%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

Built by AfrexAI — AI context packs for business operators who ship.

Get the full toolkit:

AI Revenue Leak Calculator — Find where you're losing money
Context Packs — Industry-specific AI agent configs ($47/pack)
Agent Setup Wizard — Deploy your first AI agent in 15 minutes

Bundles: Playbook $27 | Pick 3 for $97 | All 10 for $197 | Everything Bundle $247
## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: 1kalin
- Version: 1.0.0
## Source health
- Status: healthy
- Source download looks usable.
- Yavira can redirect you to the upstream package for this source.
- Health scope: source
- Reason: direct_download_ok
- Checked at: 2026-04-23T16:43:11.935Z
- Expires at: 2026-04-30T16:43:11.935Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/afrexai-revenue-forecasting)
- [Send to Agent page](https://openagent3.xyz/skills/afrexai-revenue-forecasting/agent)
- [JSON manifest](https://openagent3.xyz/skills/afrexai-revenue-forecasting/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/afrexai-revenue-forecasting/agent.md)
- [Download page](https://openagent3.xyz/downloads/afrexai-revenue-forecasting)