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 TrueClicks/claude-plugins --skill skill-162-creative-asset-quality-ai-campaign-optimizationgit clone --depth 1 https://github.com/TrueClicks/claude-pluginsWrote 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/trueclicks/claude-plugins/skill-162-creative-asset-quality-ai-campaign-optimization)<a href="https://agentmods.dev/skills/trueclicks/claude-plugins/skill-162-creative-asset-quality-ai-campaign-optimization"><img src="https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-162-creative-asset-quality-ai-campaign-optimization/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/trueclicks/claude-plugins/skill-162-creative-asset-quality-ai-campaign-optimization"><img src="https://agentmods.dev/badge/skills/trueclicks/claude-plugins/skill-162-creative-asset-quality-ai-campaign-optimization.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 | $0.00032 | $0.00714 |
| Opus 5.5 | $0.00013 | $0.00286 |
| Sonnet 5.5 | $0.00006 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
skill-162-creative-asset-quality-ai-campaign-optimization 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 21d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill 162: Creative Asset Quality for AI Campaign Optimization
Purpose
Audit creative asset coverage and quality for AI-driven campaigns (PMax, Demand Gen, AI Max for Search). AI campaigns can only optimize effectively when given sufficient creative variety - limited assets constrain machine learning's ability to test combinations and find optimal assemblies.
Data Requirements
Data Source: Standard
Standard Data:
data/account/campaigns/*/ad_groups/*/ads.md- RSA headlines and descriptionsdata/account/campaigns/*/campaign.md- Campaign types (identify PMax, Demand Gen)
Reference GAQL (Asset Performance):
SELECT
asset.name,
asset.type,
asset.text_asset.text
FROM asset_group_asset
WHERE asset_group_asset.status = 'ENABLED'
Use /google-ads:get-custom for asset-level performance ratings and PMax asset group details.
Analysis Steps
- Inventory text assets: Count headlines and descriptions, assess diversity (keyword, benefit, CTA, proof, urgency themes)
- Assess image coverage: Check for landscape (1.91:1), square (1:1), portrait (4:5), and logo variations
- Check video availability: Verify custom videos vs. auto-generated, multiple aspect ratios
- Review asset performance: Identify "Low" rated assets needing replacement
- Evaluate asset group structure: For PMax, verify distinct themes per asset group
Thresholds
| Condition | Severity |
|---|---|
| Missing required image ratio (portrait 4:5 for PMax) | Critical |
| No custom video (using auto-generated only) | Critical |
| <10 unique headlines in RSA/PMax | Warning |
| 3+ headlines with same theme (redundant) | Warning |
| Assets rated "Low" for >30 days | Warning |
| Missing wide logo (4:1) | Info |
Output
Short format (default):
## Creative Asset Audit
**Account:** [Name] | **AI campaigns:** [X] | **Asset gaps:** [Y]
### Critical ([Count])
- **[Campaign]**: Missing portrait images (4:5) -> Create 3-5 images
- **[Campaign]**: No custom video -> Produce 10-30 sec video
### Warnings ([Count])
- **Headlines**: [X] redundant themes -> Replace with diverse messaging
- **[Asset]**: Rated "Low" -> Replace with fresh creative
### Asset Coverage
| Type | Required | Provided | Gap |
|------|----------|----------|-----|
| Headlines | 10-15 | X | [Y] |
| Landscape | 3+ | X | [Y] |
| Portrait | 3+ | X | [Y] |
| Video | 1+ | X | [Y] |
### Recommendations
1. Create [asset type] - Expected +[X]% delivery
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.
- 21d ago First seen · 81 lines · 32 tokens per session scan A 4ee78d5b4b81
skill-162-creative-asset-quality-ai-campaign-optimization is a skill published in the GitHub repository TrueClicks/claude-plugins (2 stars, last pushed 22d ago), licensed MIT. It adds 32 tokens to every session and 714 once invoked, about $0.0001 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 2026-10-08.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…