ads

ads is a skill for Claude Code, Codex from mrDesign-ww/vault-os. It costs 175 tokens per session (5,915 once invoked), scanned A, a copy of ads, MIT.

A guide for creating and managing paid advertising campaigns on platforms such as Google Ads, Meta, LinkedIn, and X. It covers goals, budgets, audiences, offers, and campaign results.

In plain words
What is it for?
Use it to plan or improve paid campaigns, including search and social ads, audience targeting, retargeting, customer acquisition, and budget performance.
Why use it?
It helps structure the information needed to plan ads and avoid making campaign decisions without clear goals, targeting, or budget limits.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to plan or improve paid campaigns, including search and social ads, audience targeting, retargeting, customer acquisition, and budget performance.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mrdesign-ww/vault-os/ads
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 mrDesign-ww/vault-os --skill ads
Clone the repo
git clone --depth 1 https://github.com/mrDesign-ww/vault-os

Made for: Claude Code, Codex.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/ads/github.svg)](https://agentmods.dev/skills/mrdesign-ww/vault-os/ads)
Your own site
<a href="https://agentmods.dev/skills/mrdesign-ww/vault-os/ads"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/ads/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.

agentmods 80×15 button for ads

Your own site · 80×15
<a href="https://agentmods.dev/skills/mrdesign-ww/vault-os/ads"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/ads.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 175 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,915 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.
Origin 86% copy Near-identical to another mod 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 $0.00175 $0.05915
Opus 5 $0.00088 $0.02958
Sonnet 5 $0.00035 $0.01183
Haiku 4.5 $0.00017 $0.00592

Measured 7d ago against content hash 8be65c0dfa27, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

ads 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 7d 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.

Origin

This is a copy

86% identical to ads — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

shell/claude/skills/ads/SKILL.md · 488 lines

How it starts

The opening of the file, as written. The whole thing — 488 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an expert performance marketer with direct access to ad platform accounts. Your goal is to help create, optimize, and scale paid advertising campaigns that drive efficient customer acquisition.

Before Starting

Check for product marketing context first: If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.

Gather this context (ask if not provided):

1. Campaign Goals

  • What's the primary objective? (Awareness, traffic, leads, sales, app installs)
  • What's the target CPA or ROAS?
  • What's the monthly/weekly budget?
  • Any constraints? (Brand guidelines, compliance, geographic)

2. Product & Offer

  • What are you promoting? (Product, free trial, lead magnet, demo)
  • What's the landing page URL?
  • What makes this offer compelling?

3. Audience

  • Who is the ideal customer?
  • What problem does your product solve for them?
  • What are they searching for or interested in?
  • Do you have existing customer data for lookalikes?

4. Current State

  • Have you run ads before? What worked/didn't?
  • Do you have existing pixel/conversion data?
  • What's your current funnel conversion rate?

Reference Routing

This skill's depth lives in references — load by intent. For any operational decision on a live account (kill/keep/scale/budget), load the relevant playbook before answering; the thresholds live there, not here.

User intent Load Covers
B2B strategy, funnel stages, budget splits, kill rules, lead quality, breakeven math b2b-paid-playbook.md Demand lifecycle, leading/lagging signals, kill rules, offline conversion loop, U/B/F lead scoring, scaling quadrant
Meta operations: when to kill/graduate/scale an ad, fatigue, testing structure meta-decision-system.md TCPL-anchored decision tree, ad-count ceiling, 80/20 CBO structure, fatigue bands, lead forms, Advantage+ transition
LinkedIn operations: bidding, audience sizing, scaling, benchmarks, TLAs, formats linkedin-b2b-playbook.md Bidding progression, penetration scaling, sizing rules, funnel benchmarks, document/conversation ads, audit shortlist
Google Search: what to spend on first, structure, match types, negatives, PMax google-search-playbook.md Intent ladder, account structure, match-type gates, negatives, bidding by volume, offline conversions, PMax guardrails
Named-account targeting, pipeline acceleration, cross-channel retargeting abm-playbook.md LinkedIn/Meta ABM, list mechanics, acceleration campaigns, UTM cross-channel remarketing, ABM measurement
Generating Google RSAs rsa-output-spec.md Mandatory output spec — limits, sidecars, template, self-check
Audience setup, tracking setup, launch checklists, copy formulas audience-targeting.md · conversion-tracking.md · platform-setup-checklists.md · ad-copy-templates.md Existing foundations

Read the full file on GitHub · 488 lines

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. 7d ago First seen · 488 lines · 175 tokens per session scan A 8be65c0dfa27

Subscribe to this mod's changes

ads is a skill published in the GitHub repository mrDesign-ww/vault-os (2 stars, last pushed 4d ago), licensed MIT. It adds 175 tokens to every session and 5,915 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to ads, differing in 26 lines, and is treated as a copy.

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