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 anthropic-brandgit 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/anthropic-brand)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/anthropic-brand"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/anthropic-brand.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 | $0.00023 | $0.00340 |
| Opus 5 | $0.00012 | $0.00170 |
| Sonnet 5 | $0.00005 | $0.00068 |
| Haiku 4.5 | $0.00002 | $0.00034 |
Grade A, and why
anthropic-brand 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 3d 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
Anthropic Brand Design System
Color Palette (from anthropic.com)
| Token Name | Hex | Usage |
|---|---|---|
| Identity Light | #F5F4ED |
Page/poster backgrounds, light fills |
| Corporate Dark | #141413 |
Primary text, dark surfaces, casing |
| Primary Brand Accent | #D97757 |
Interactive highlights, connectors |
| Secondary Brand Accent | #EDA100 |
Supporting accent, warm highlights |
| Tertiary Brand Accent | #A18A74 |
Substrate fills, muted elements |
| Muted Mid Gray | #B0ADA5 |
Annotation lines, secondary text |
Typography
- Heading/Display: Anthropic uses a clean serif via
var(--serif)CSS variable on their site. The publicly served font resolves to a refined serif face. When unavailable, use a clean sans-serif like DejaVu Sans as fallback. - Body/UI: Clean sans-serif via
var(--sans). - Monospace: For code/technical labels.
Design Principles
- Minimalist: Low saturation, warm tones, generous whitespace
- No neon/gradients: Avoid high-intensity AI-style gradients
- Warm palette: Cream backgrounds, earthy accents, muted neutrals
- Clean geometry: Simple shapes, thin annotation lines
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.
- 3d ago First seen · 31 lines · 23 tokens per session scan A d2c76f3d89eb
anthropic-brand is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 340 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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