Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/rajitsaha/100xprismnpx agentmods add skills/rajitsaha/100xprism/ad-creativeWrote 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/rajitsaha/100xprism/ad-creative)<a href="https://agentmods.dev/skills/rajitsaha/100xprism/ad-creative"><img src="https://agentmods.dev/badge/skills/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 5.1 | $0.00077 | $0.02884 |
| Opus 5 | $0.00039 | $0.01442 |
| Sonnet 5 | $0.00015 | $0.00577 |
| Haiku 4.5 | $0.00008 | $0.00288 |
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 8d 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.
This is a copy
84% identical to ad-creative — 165 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
Google Ads (Responsive Search Ads)
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 319 lines · 77 tokens per session scan A d647caaac2bf
ad-creative is a skill published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 8d ago), licensed MIT. It adds 77 tokens to every session and 2,884 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to ad-creative, differing in 165 lines, and is treated as a copy.
Other skills, from other repositories
ad-campaign-analyzer
Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.
google-analytics
Skill "google-analytics" from VKirill/claude-lane-stack, covering version requirements (may 2026), usage, use this skill when, do not use this skill when and purpose.
google-tag-manager
Skill "google-tag-manager" from VKirill/claude-lane-stack, covering 🎯 version requirements (june 2026), usage, use this skill when, do not use this skill when and purpose.
analyzing-windows-lnk-files-for-artifacts
Parse Windows LNK shortcut files to extract target paths, timestamps, volume information, and machine identifiers for forensic timeline reconstruction.
analyzing-command-and-control-communication
Analyzes malware command-and-control (C2) communication protocols to understand beacon patterns, command structures, data encoding, and infrastructure. Covers HTTP, HTTPS, DNS, and custom protocol C2 analysis for detection development and threat intelligence. Activates for requests involving C2 analysis, beacon…
analyzing-memory-forensics-with-lime-and-volatility
Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.