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 agentmods add skills/craftos-dev/craftbot/paid-adsnpx skills add CraftOS-dev/CraftBot --skill paid-adsgit clone --depth 1 https://github.com/CraftOS-dev/CraftBotWrote 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/craftos-dev/craftbot/paid-ads)<a href="https://agentmods.dev/skills/craftos-dev/craftbot/paid-ads"><img src="https://agentmods.dev/badge/skills/craftos-dev/craftbot/paid-ads.svg" alt="Measured on agentmods" 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 | $0.00093 | $0.03620 |
| Opus 5 | $0.00046 | $0.01810 |
| Sonnet 5 | $0.00019 | $0.00724 |
| Haiku 4.5 | $0.00009 | $0.00362 |
Grade A, and why
paid-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 2d 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.
This is a copy
100% identical to paid-ads — 0 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.
How it starts
The opening of the file, as written. The whole thing — 552 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid Ads
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
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?
- Any promotions or urgency elements?
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?
- Any existing creative assets?
Platform Selection Guide
Google Ads
Best for: High-intent search traffic, capturing existing demand Use when:
- People actively search for your solution
- You have clear keywords with commercial intent
- You want bottom-of-funnel conversions
Campaign types:
- Search: Keyword-targeted text ads
- Performance Max: AI-driven cross-channel
- Display: Banner ads across Google network
- YouTube: Video ads
- Demand Gen: Discovery and Gmail placements
Meta (Facebook/Instagram)
Best for: Demand generation, visual products, broad targeting Use when:
- Your product has visual appeal
- You're creating demand (not just capturing it)
- You have strong creative assets
- You want to build audiences for retargeting
Campaign types:
- Advantage+ Shopping: E-commerce automation
- Lead Gen: In-platform lead forms
- Conversions: Website conversion optimization
- Traffic: Link clicks to site
- Engagement: Social proof building
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.
- 2d ago First seen · 552 lines · 93 tokens per session scan A 0d00d270771f
paid-ads is a skill published in the GitHub repository CraftOS-dev/CraftBot (378 stars, last pushed 3d ago), licensed MIT. It adds 93 tokens to every session and 3,620 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to paid-ads, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
turix-mac
Computer Use Agent (CUA) for macOS automation using TuriX. Use when you need to perform visual tasks on the desktop, such as opening apps, clicking buttons, or navigating UIs that don't have a CLI or API.
browser-control
Control web pages in an existing Chromium remote-debugging session with deterministic sagui commands or the browser-only AI Agent. Use when Claude Code needs to inspect tabs, observe semantic elements, navigate, click, type, send keys, scroll, or complete a browser goal through CDP without controlling native macOS UI…
agent-smoke-test
Run manual development checks for SwiftAutoGUI AI Agent actions through the local sagui CLI. Use when Claude or Codex needs to test app-control or Accessibility BasicAction generation on macOS.
macos-control
Control macOS GUI applications via mouse automation, keyboard input, screenshots, image recognition, and AppleScript execution. Use when you need to interact with macOS app UIs, take screenshots, click buttons, type text, scroll, drag, or locate images on screen.
artifact-storage-policy
管理 Codex 交付物的專案識別、Google Drive／GitHub 路由、檔名、儲存位置與回讀驗證；凡工作報告、外部保存、交付成果或新 repository 選擇涉及遠端儲存時使用。.
model-worker-routing
Route a task to the existing Ox Alpha worker only when the user explicitly names Ox Alpha; preserve normal Codex behavior for all other tasks.