Revenue Engine

Money-Making Overview

This engine is the financial nervous system of your business. Without it, you leak 5-15% of revenue through forgotten invoices, missed renewals, and untracked metrics. With it, you capture every dollar and know exactly where to focus to grow.

Dependencies

• Python 3.8+ or Node.js 18+
• Access to relevant APIs/services for your specific use case
• Basic understanding of the domain concepts

Commands

# Refer to the skill's usage section for specific commands
# Adapt these to your workflow

Revenue Streams

1. Your Own Revenue Ops — tracking prevents leakage
2. Revenue Dashboard Service ($2K-5K/setup) — build for clients
3. CFO-as-a-Service ($1K-3K/mo) — ongoing financial monitoring

First Action in 60 Minutes

#!/usr/bin/env bash
# Revenue engine health check
mkdir -p ~/revenue-engine/{metrics,reports,alerts}

echo "=== Revenue Health Check ==="
echo "MRR: $(curl -s https://api.stripe.com/v1/subscriptions --header "Authorization: Bearer $STRIPE_KEY" 2>/dev/null | python3 -c "import json,sys; d=json.load(sys.stdin); print(sum(int(i['plan']['amount']) for i in d['data'])/100)" 2>/dev/null || echo 'Set \$STRIPE_KEY')"
echo ""
echo "Metrics to track this week:"
echo "  - MRR growth rate"
echo "  - Churn rate (exits / total)"
echo "  - LTV (avg revenue × avg months retained)"
echo "  - CAC (total sales cost / new customers)"
echo "  - Quick Ratio (new MRR + expansion MRR) / churned MRR"

Revenue Pipeline Architecture

Revenue Engine is a foundational core infrastructure skill that provides system foundation capabilities for the agent ecosystem.

• Input layer — Receives and validates incoming requests
• Processing layer — Core logic for system foundation
• Output layer — Formats and delivers results
• State management — Maintains context across invocations

Revenue Configuration & Integration

Trigger phrases:

• "revenue engine"
• "Manage revenue pipelines, track Stripe/analytics metrics, and automate financial"

Set up required environment variables and paths, configure logging level and output format, define resource limits (memory, time, API calls), enable/disable features via configuration flags.

• Exposes standard interfaces for other skills to consume
• Supports event-driven and request-response patterns
• Compatible with the 1ai-skills hook system
• Logs metrics for the skill performance monitor

Revenue Intelligence & Model Routing

Different revenue tasks need different models. Route intelligently:

# Example: Model routing
ROUTES = {
    "code": ["claude-sonnet-4-20250514", "gpt-4o"],
    "vision": ["gemini-2.5-pro", "gpt-4o"],
    "fast": ["gemini-2.5-flash", "gpt-4o-mini"],
}

def route_request(task: str, prompt: str):
    models = ROUTES.get(task, ROUTES["fast"])
    for model in models:
        try:
            return call_model(model, prompt)
        except Exception:
            continue
    raise RuntimeError("All models failed")

When to Deploy Revenue Engine

• When the task falls within this skill's domain expertise
• When automated execution saves time over manual work
• When the skill's tools and integrations are available

When NOT to Deploy

• When the task can be solved with existing standard libraries
• When the infrastructure is already in place and working
• When the added complexity does not provide measurable benefit

Revenue Verification Protocol

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run revenue engine workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification Checklist

• All steps executed successfully
• Results validated against acceptance criteria
• Error handling tested with edge cases
• Documentation updated with findings

Anti-Rationalization Table

Output Format

On completion: "MRR: $[N], Churn: [N]%, LTV: $[N], CAC: $[N], Quick Ratio: [N], Revenue Health: [GREEN/YELLOW/RED]"

When to Use

Use this skill when working with revenue engine.

Workflow

See the parent skill for authoritative workflow documentation.