Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Ad-Superpowers/ad-superpowers-plugin --skill ltv-cac-modeling-frameworkgit clone --depth 1 https://github.com/Ad-Superpowers/ad-superpowers-pluginWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/ltv-cac-modeling-framework)<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/ltv-cac-modeling-framework"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/ltv-cac-modeling-framework/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/ltv-cac-modeling-framework"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/ltv-cac-modeling-framework.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00110 | $0.04666 |
| Opus 5.5 | $0.00044 | $0.01866 |
| Sonnet 5.5 | $0.00022 | $0.00933 |
| Haiku 4.5 | $0.00011 | $0.00467 |
Grade A, and why
ltv-cac-modeling-framework scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 468 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LTV:CAC Modeling Framework
Purpose
Provide advertisers and agencies with a rigorous framework for connecting customer lifetime value to acquisition cost, enabling profitable scaling decisions. Stop optimizing for cheap conversions and start optimizing for valuable customers.
When to Use This Skill
Invoke when user mentions:
- LTV/CLV: "What's the lifetime value of my customers?"
- CAC: "How much should I spend to acquire a customer?"
- CPA targets: "What CPA is profitable?"
- Budget ceilings: "How much can I afford to spend on ads?"
- Payback period: "How long until I recover my acquisition cost?"
- MER: "What's my marketing efficiency ratio?"
- Cohort analysis: "How do customers from different channels compare?"
- Sustainability: "Is my ad spend sustainable long-term?"
Part 1: LTV Calculation Methods
Method 1: Historic LTV (Simplest)
Historic LTV = Total Revenue from Customer Segment / Number of Customers in Segment
Example:
Total revenue from 2024 customers: €500,000
Number of customers acquired in 2024: 2,000
Historic LTV = €500,000 / 2,000 = €250
Pros: Simple, uses real data. Cons: Backward-looking, doesn't account for future purchases, skewed by outliers.
When to use: Early-stage businesses with 6-12 months of data as a starting baseline.
Method 2: Predictive LTV (Recommended)
Predictive LTV = (Average Order Value) × (Purchase Frequency per Year) × (Customer Lifespan in Years)
Example:
AOV: €65
Purchases per year: 3.2
Average customer lifespan: 2.5 years
Predictive LTV = €65 × 3.2 × 2.5 = €520
Enriched formula (accounts for margin):
Gross Profit LTV = AOV × Gross Margin % × Purchase Frequency × Customer Lifespan
Example:
AOV: €65, Margin: 55%, Frequency: 3.2/yr, Lifespan: 2.5yr
Gross Profit LTV = €65 × 0.55 × 3.2 × 2.5 = €286
This is the number that matters for ad decisions — LTV based on gross profit, not revenue.
Method 3: Cohort-Based LTV (Most Accurate)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 468 lines · 110 tokens per session scan A 26a29728adfb
ltv-cac-modeling-framework is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 110 tokens to every session and 4,666 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-11.
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