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 rules/rajitsaha/100xprism/ad-creativegit clone --depth 1 https://github.com/rajitsaha/100xprismWrote 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/rules/rajitsaha/100xprism/ad-creative)<a href="https://agentmods.dev/rules/rajitsaha/100xprism/ad-creative"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/ad-creative.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 | $0.00029 | $0.02851 |
| Opus 5 | $0.00015 | $0.01425 |
| Sonnet 5 | $0.00006 | $0.00570 |
| Haiku 4.5 | $0.00003 | $0.00285 |
Grade A, and why
ad-creative 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 today.
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 — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Creative
Generate high-performing ad creative at scale — headlines, descriptions, and primary text — and iterate on real performance data.
Before Starting
Product context: If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it first and tailor output to it; only ask for what it doesn't cover.
Gather this context (ask if not provided):
1. Platform & Format
- Platform? (Google Ads, Meta, LinkedIn, TikTok, Twitter/X)
- Ad format? (Search RSAs, display, social feed, stories, video)
- Existing ads to iterate on, or starting from scratch?
2. Product & Offer
- What are you promoting? (Product, feature, free trial, demo, lead magnet)
- Core value proposition?
- Differentiation from competitors?
3. Audience & Intent
- Target audience?
- Stage of awareness? (Problem-aware, solution-aware, product-aware)
- Driving pain points or desires?
4. Performance Data (if iterating)
- What creative is currently running?
- Best-performing headlines/descriptions? (CTR, conversion rate, ROAS)
- Underperformers?
- Angles or themes already tested?
5. Constraints
- Brand voice guidelines or words to avoid?
- Compliance requirements? (Industry regulations, platform policies)
- Mandatory elements? (Brand name, trademark symbols, disclaimers)
How This Skill Works
Two modes:
- Mode 1: Generate from Scratch — full set of ad creative from product context, audience insights, and platform best practices.
- Mode 2: Iterate from Performance Data — given data (CSV, paste, or API output), analyze what's working, find patterns in top performers, and generate variations that build on winning themes while exploring new angles.
Core loop:
Pull performance data → Identify winning patterns → Generate new variations → Validate specs → Deliver
Platform Specs
Platforms reject or truncate creative over these limits — verify every piece of copy fits before delivering.
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.
- today First seen · 319 lines · 2,851 tokens per session scan A 5d053b2d3623
ad-creative is a cursor rule published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 4d ago), licensed MIT. It adds 29 tokens to every session and 2,851 once invoked, about $0.0001 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 2026-09-03.
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