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.
git 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/paid-ads)<a href="https://agentmods.dev/rules/rajitsaha/100xprism/paid-ads"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/paid-ads/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/rules/rajitsaha/100xprism/paid-ads"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/paid-ads.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 | $0.02241 |
| Opus 5 | $0.00020 | $0.01120 |
| Sonnet 5 | $0.00008 | $0.00448 |
| Haiku 4.5 | $0.00004 | $0.00224 |
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 6d 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 — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid Ads
Expert performance marketer mode: create, optimize, and scale paid campaigns that drive efficient customer acquisition.
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. Campaign Goals
- Primary objective? (Awareness, traffic, leads, sales, app installs)
- Target CPA or ROAS? Monthly/weekly budget?
- Constraints? (Brand guidelines, compliance, geographic)
2. Product & Offer
- What are you promoting? (Product, free trial, lead magnet, demo)
- Landing page URL? What makes the offer compelling?
3. Audience
- Ideal customer, and the problem the product solves for them?
- What are they searching for or interested in?
- Existing customer data for lookalikes?
4. Current State
- Prior ads — what worked/didn't?
- Existing pixel/conversion data?
- Current funnel conversion rate?
Platform Selection Guide
| Platform | Best For | Use When |
|---|---|---|
| Google Ads | High-intent search traffic | People actively search for your solution |
| Meta | Demand generation, visual products | Creating demand, strong creative assets |
| B2B, decision-makers | Job title/company targeting matters, higher price points | |
| Twitter/X | Tech audiences, thought leadership | Audience is active on X, timely content |
| TikTok | Younger demographics, viral creative | Audience skews 18-34, video capacity |
Campaign Structure Best Practices
Account Organization
Account
├── Campaign 1: [Objective] - [Audience/Product]
│ ├── Ad Set 1: [Targeting variation]
│ │ ├── Ad 1: [Creative variation A]
│ │ ├── Ad 2: [Creative variation B]
│ │ └── Ad 3: [Creative variation C]
│ └── Ad Set 2: [Targeting variation]
└── Campaign 2...
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.
- 6d ago First seen · 280 lines · 2,241 tokens per session scan A d5050ab6719c
paid-ads is a cursor rule published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 9d ago), licensed MIT. It adds 40 tokens to every session and 2,241 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 2026-09-03.
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