ads-math

ads-math is a skill for Claude Code from AgriciDaniel/claude-ads. It costs 75 tokens per session (231 once invoked), scanned A, original, MIT.

A calculator and financial model for paid advertising metrics and decisions. It covers measures such as CPA, or cost per acquisition; ROAS, or revenue earned per ad-cost unit; and LTV:CAC, or customer lifetime value compared with acquisition cost.

In plain words
What is it for?
Use it for break-even analysis, ad-cost calculations, budget forecasts, return estimates, contribution-margin checks, and experiment economics.
Why use it?
It makes the assumptions and formulas visible and avoids misleading forecasts when inputs, time periods, currencies, or margins do not match.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the claude-ads plugin — 30 skills, 25 agents shipped together

Good fit Use it for break-even analysis, ad-cost calculations, budget forecasts, return estimates, contribution-margin checks, and experiment economics.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/agricidaniel/claude-ads/ads-math
About the project

Claude Ads is a Claude Code skill for managing paid-media operations across 12 advertising platforms, using account data or exports to produce audits, plans, creative workflows, experiments, monitoring, and reports. Agencies, consultants, and in-house performance teams use it for source-based analysis and controlled account work. The catalogue entries are its platform-specific skills, workers, and supporting instructions.

AgriciDaniel/claude-ads · 8,921 stars · on GitHub · claude-ads.md

Install

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.

Any agent
npx skills add AgriciDaniel/claude-ads --skill ads-math
Clone the repo
git clone --depth 1 https://github.com/AgriciDaniel/claude-ads

Made for: Claude Code.

Or install claude-ads, the plugin that ships this one along with the rest of its 30 skills, 25 agents.

Wrote 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.

agentmods badge for ads-math

README.md
[![agentmods](https://agentmods.dev/badge/skills/agricidaniel/claude-ads/ads-math.svg)](https://agentmods.dev/skills/agricidaniel/claude-ads/ads-math)
Your own site
<a href="https://agentmods.dev/skills/agricidaniel/claude-ads/ads-math"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-ads/ads-math.svg" alt="Measured on agentmods" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 231 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 20 May 2026
  • Snyk pass 20 May 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00075 $0.00231
Opus 5 $0.00037 $0.00115
Sonnet 5 $0.00015 $0.00046
Haiku 4.5 $0.00007 $0.00023

Measured 8d ago against content hash b129b371f983, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

ads-math 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 8d 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.

skills/ads-math/SKILL.md · 22 lines

What it actually says

  1. Identify the decision and collect units, currency, period, tax/refund treatment, margin, attribution basis, and uncertainty.
  2. Show the formula and map every input to an operator value or cited artifact.
  3. Validate denominators, sign, missing values, incompatible windows, and unit conversions.
  4. Calculate base, downside, and upside cases where uncertainty affects the decision.
  5. Keep platform-attributed revenue, blended business revenue, cash flow, and contribution margin distinct.
  6. Return machine-readable inputs, formulas, outputs, sensitivities, and decision implications.

Never fabricate missing financial inputs, hide division-by-zero, or present a point forecast without its assumptions.

Changes

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.

  1. 8d ago First seen · 22 lines · 75 tokens per session scan A b129b371f983

Subscribe to this mod's changes

ads-math is a skill published in the GitHub repository AgriciDaniel/claude-ads (8,921 stars, last pushed 1mo ago), licensed MIT. It adds 75 tokens to every session and 231 once invoked, about $0.0004 per session on Opus 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 2026-08-30.

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