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 cxcscmu/SkillLearnBench --skill image-generation-brandinggit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/image-generation-branding)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/image-generation-branding"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/image-generation-branding.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 | $0.00022 | $0.00154 |
| Opus 5 | $0.00011 | $0.00077 |
| Sonnet 5 | $0.00004 | $0.00031 |
| Haiku 4.5 | $0.00002 | $0.00015 |
Grade A, and why
image-generation-branding 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 4d 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.
What it actually says
Branding Principles
- Minimalism: Avoid gradients, shadows, or high-saturation colors unless explicitly requested.
- Palette Control: Utilize strict hex-based mapping for corporate components.
- Visual Hierarchy: Clearly separate technical layers using consistent line weights (Muted Mid Gray).
- Typography: Use sans-serif, low-weight headers for technical documentation.
Implementation Checklist
- Identify all required components (e.g., Casing, PCB, Battery).
- Map components to requested color tokens.
- Ensure background matches the Brand Identity Light.
- Final Review: Compare against core brand tokens for saturation and style adherence.
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.
- 4d ago First seen · 17 lines · 22 tokens per session scan A 67b3ad6e949f
image-generation-branding is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 154 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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