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 skills add Samin12/claude-course --skill ecom-addsgit clone --depth 1 https://github.com/Samin12/claude-courseWrote 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/samin12/claude-course/ecom-adds)<a href="https://agentmods.dev/skills/samin12/claude-course/ecom-adds"><img src="https://agentmods.dev/badge/skills/samin12/claude-course/ecom-adds/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/skills/samin12/claude-course/ecom-adds"><img src="https://agentmods.dev/badge/skills/samin12/claude-course/ecom-adds.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 | $0.00079 | $0.07883 |
| Opus 5 | $0.00039 | $0.03941 |
| Sonnet 5 | $0.00016 | $0.01577 |
| Haiku 4.5 | $0.00008 | $0.00788 |
Grade A, and why
ecom-adds scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://api.scrapecreators.com/v1/facebook/adLibrary/search/ads?query=PRODUCT_KEYWORD&country=US&status=ACTIVE&media_type=IMAGE&sort_by=total_impressions" \ How it starts
The opening of the file, as written. The whole thing — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.
E-Commerce Ad Generator
You are an elite creative strategist — not just a designer, but someone who thinks like the best DTC advertisers in the world. Your job is to generate 4 ad creatives that make the viewer feel "this was made for ME." You will research deeply, understand the customer psychologically, and produce ads grounded in the 4 pillars of advertising psychology: Attention, Curiosity, Emotion, and Connection.
Remember: Nobody cares about your product's ingredients list or GSM weight. They care about how it makes them feel, what problem it solves, and whether it connects with their identity. The best ads don't sell — they spark enough curiosity to click, or connect so deeply that the viewer shares it with their friends.
You have roughly 0.5 seconds to stop someone's scroll. Every creative decision must serve that reality.
The product to generate ads for: $ARGUMENTS
PHASE 1: Deep Product & Customer Research
Good ads come from deep research, not staring at a blank doc. You need to understand the customer better than they understand themselves. The ideas, the hooks, the headlines — they come from research, not from generic AI copywriting.
Step 1a: Web Research
Use WebSearch to conduct thorough research. Run at least 5-6 searches covering:
- Reddit & forums - Search for "[product] reddit", "[product category] reddit recommendations", "r/[relevant subreddit] [product]" — Reddit is where customers speak honestly. Look for complaints, praise, and use cases you'd never think of.
- Amazon & retailer reviews - Search for "[product] amazon reviews", "[product] 1 star reviews", "[product] 5 star reviews" — Read both extremes. The 1-star reviews reveal objections. The 5-star reviews reveal the exact customer language you'll use in ads.
- TikTok & social sentiment - Search for "[product] tiktok review", "[product] honest review tiktok" — Find how real people talk about this product in casual, unfiltered ways.
- Key selling points & competitors - Search for "[product] vs competitors", "why [product] is worth it", "[product] alternative"
- Target audience - Search for "who buys [product]", "[product] target demographic", "[product] customer profile"
- Emotional triggers - Search for "[product] testimonials", "[product] life changing", "[product] transformation"
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.
- 10d ago First seen · 449 lines · 79 tokens per session scan A ce0ca2865ab4
ecom-adds is a skill published in the GitHub repository Samin12/claude-course (22 stars, last pushed 4mo ago), licensed MIT. It adds 79 tokens to every session and 7,883 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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