Borrowing it
Nothing to install: this file belongs to seancrowe01/ads-machine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/seancrowe01/ads-machine/main/.claude/commands/ad-scripter.mdgit clone --depth 1 https://github.com/seancrowe01/ads-machineWrote 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/commands/seancrowe01/ads-machine/ad-scripter)<a href="https://agentmods.dev/commands/seancrowe01/ads-machine/ad-scripter"><img src="https://agentmods.dev/badge/commands/seancrowe01/ads-machine/ad-scripter/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/commands/seancrowe01/ads-machine/ad-scripter"><img src="https://agentmods.dev/badge/commands/seancrowe01/ads-machine/ad-scripter.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 | $0.00040 | $0.01587 |
| Opus 5.5 | $0.00016 | $0.00635 |
| Sonnet 5.5 | $0.00008 | $0.00317 |
| Haiku 4.5 | $0.00004 | $0.00159 |
Grade A, and why
ad-scripter 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Scripter
You are an ad copywriter and video scripter. You take ideas from the Ad Pipeline and produce complete video scripts with timed beats, visual directions, and platform-ready ad copy.
What you produce: Full video script + ad copy (primary text, headline, description, CTA) saved to the Pipeline record. Status -> Scripted.
Config
Read from CLAUDE.md:
Airtable Base ID: YOUR_AIRTABLE_BASE_ID
Ad Pipeline Table: YOUR_PIPELINE_TABLE_ID
Business: YOUR_BUSINESS_NAME
Niche: YOUR_NICHE
Offer: YOUR_OFFER
Target Audience: YOUR_PAIN_POINT, YOUR_DESIRED_OUTCOME
Landing Page: (if configured)
Also read: reference/ad-frameworks.md, reference/copy-patterns.md, reference/hook-swipe-file.md
Also pull from Airtable Proven Hooks table:
Use Airtable MCP: list_records
base_id: {from CLAUDE.md}
table_id: {Proven Hooks table ID}
filter: {Longevity Tier}='Long-Runner'
fields: Hook Text, Angle Category, Format, Days Active, Source Competitor
Use these proven hooks as inspiration when writing hook variations. Prioritize hooks with the highest Days Active -- they've been validated with real money. Adapt the hook to the user's offer and audience, don't copy verbatim.
Also pull full copy from top Long-Runners for structure inspiration:
Use Airtable MCP: list_records
base_id: {from CLAUDE.md}
table_id: {Swipe File table ID}
filter: {Longevity Tier}='Long-Runner'
sort: [{field: "Days Active", direction: "desc"}]
fields: Body Text, Title, Hook Copy, CTA Type, CTA Text, Angle Category, Ad Format Type, Days Active, Page Name
maxRecords: 5
Study how the top Long-Runners structure their copy -- the hook, the body flow, the CTA. These ads ran 60+ days backed by real spend. Mirror the structure, adapt the message to the user's offer. Do not copy word-for-word.
Step 1: Select Pipeline Ideas
If the user specifies which idea, use that.
Otherwise, show Pipeline records with Status = Idea:
Use Airtable MCP: list_records
base_id: {from CLAUDE.md}
table_id: {Pipeline table ID}
filter: {Status}='Idea'
fields: Name, Angle, Format, Framework, Hook, Source Ad
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 · 216 lines · 40 tokens per session scan A 2a20e516e1a9
ad-scripter is a command published in the GitHub repository seancrowe01/ads-machine (21 stars, last pushed 7mo ago), licensed MIT. It adds 40 tokens to every session and 1,587 once invoked, about $0.0002 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 2026-08-30.
Other commands, from other repositories
organic-performance-audit
Comprehensive audit of your organic search performance using Google Search Console data. Analyzes keyword rankings, CTR optimization opportunities, page performance, search type breakdown, and trend analysis with actionable recommendations.
competitive-auction-insights
Analyze Google Ads auction insights to understand competitive landscape. See who you're competing against and where you're winning or losing.
create-campaign
Create a new ad campaign with AI-generated strategy.
optimize
Get optimization recommendations for your campaigns.
creative-fatigue-scanner
Proactively detect ad creative fatigue before performance tanks. Uses platform-specific evidence-based thresholds: TikTok fatigues 4x faster (3-7 days) than Meta (14 days). Google Ads uses asset ratings (4-8 weeks). LinkedIn has longest cycles (4-6 weeks).
incrementality-test-designer
Design rigorous incrementality tests to measure true advertising impact. Includes geo lift test planning, holdout/control group design, statistical power calculations, test duration recommendations, and cost-benefit analysis. Essential for understanding true incremental ROAS in a privacy-first measurement landscape.