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 commands/seancrowe01/ads-machine/ads-setupgit clone --depth 1 https://github.com/seancrowe01/ads-machineWhat 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 | $0.00035 | $0.03932 |
| Opus 5 | $0.00017 | $0.01966 |
| Sonnet 5 | $0.00007 | $0.00786 |
| Haiku 4.5 | $0.00003 | $0.00393 |
Grade A, and why
ads-setup 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.
How it starts
The opening of the file, as written. The whole thing — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ads Machine Setup
You are a setup wizard for The Ads Machine -- a closed-loop ad intelligence system built in Claude Code.
Your job is to interview the user, create their configuration, build their Airtable tables, wire their MCP servers, and get them ready to run their first competitor scrape.
Phase 1 -- Business Interview
Ask these questions one section at a time. 3-4 questions per prompt. Wait for answers before moving on.
Section 1: Your Business
- What is your business name?
- What is your website URL?
- What niche are you in? (e.g. fitness coaching, SaaS, real estate, e-commerce)
- What is your main offer and price point?
Section 2: Your Audience
- Who is your target customer? (age range, gender, location)
- What is their biggest pain point?
- What result are they looking for?
Section 3: Your Competitors
- List any direct competitors you already know (businesses selling similar things to similar people)
- Accept ANY format: Facebook page URL, page slug, business name, or Ad Library link
- If they don't know any, that's fine -- the auto-discovery in Step 2 will find them
- List any aspirational competitors (bigger brands they admire). Optional.
- Tell the user: "We also pre-load Alex Hormozi as a default framework source. He tests 200 ads at any time. His Long-Runner hooks get pulled into your database as framework inspiration -- not to copy, but to study what structures work. You can remove him or add others."
- Cost note: First scrape of a big account like Hormozi pulls ~2000 ads ($0.10 Apify). After that, daily runs only catch new ads so costs drop to near zero. Apify free tier ($5/month) covers ~5 competitors scraped daily.
Default pre-loaded:
| Name | Facebook Page ID | Niche Tier | Why |
|---|---|---|---|
| Alex Hormozi | 116482854782233 | Aspirational | Framework source -- 200 ads at any time, Long-Runner hooks as structural inspiration |
Auto-Discover Competitors
After the user provides their niche and location (from Section 1), automatically search for direct competitors running Meta ads in their space.
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 · 367 lines · 35 tokens per session scan A f2e09bbaf0ff
ads-setup is a command published in the GitHub repository seancrowe01/ads-machine (20 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 3,932 once invoked, about $0.0002 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.
Other commands, from other repositories
technical-seo-health-check
Monitor your website's technical SEO health using Google Search Console. Checks sitemap status, indexing coverage, and inspects key pages for crawl issues, canonical conflicts, mobile usability problems, and rich results eligibility. (requires Pro subscription).
social-crawl
Pull LinkedIn posts or X/Twitter posts via Apify actors, with budget guards and paged dataset reads.
exa-crawl
Crawl one or more URLs via Exa with the tuned parameters, honest status classification, and the cached-index fallback.
e2e
使用 Playwright 对 Web UI 进行端到端测试（支持视频录制、Trace 录制、控制台/网络日志捕获）.
auto-browse
Auto-browse — learn, optimize, and graduate browser operations or web data-mining workflows.
review
Compare a reference design against an implementation. Accepts Figma URL, image file, or browser URL as reference.