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 Uxcel-Lab/product-skills --skill colorgit clone --depth 1 https://github.com/Uxcel-Lab/product-skillsWrote 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/uxcel-lab/product-skills/color)<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/color"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/color/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/uxcel-lab/product-skills/color"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/color.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 | $0.00082 | $0.01915 |
| Opus 5 | $0.00041 | $0.00958 |
| Sonnet 5 | $0.00016 | $0.00383 |
| Haiku 4.5 | $0.00008 | $0.00192 |
Grade A, and why
ux-color 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 11d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Color Palette & System Skill
How this skill behaves (read first)
This is a generative skill. Color has two opposite default failure modes: arbitrary (pick a nice blue, no system, fails contrast) and garish (apply every emotional/cultural association and standout trick → the over-colored "Willy Wonka" palette aesthetics warns about). So this skill gates:
- Establish brand, audience, and market — these drive the one set of hue/emotion/culture choices worth making.
- Apply the always-true core — a restrained, role-based, tokenized, accessible palette holds for any product.
- Make the expressive choices deliberately — surface base-hue/emotion/saturation/dark-mode as gated decisions, not defaults.
Then hand off to the audits: accessibility owns contrast ratios and color-blindness; aesthetics owns harmony and balance.
Step 0 — Establish context before choosing colors
- Brand & industry — existing brand colors/guidelines? Industry conventions (finance → trust/navy; kids → playful)? Check competitors for the field's visual language.
- Audience & market — who, and where — color meaning is culture-dependent (see the emotion reference caveat).
- Personality — premium/refined vs. playful/energetic? This drives saturation and neutrals more than hue.
- Modes — light only, or dark mode too?
State assumptions if proceeding without answers.
The always-apply core (true for any palette/system)
- Build on a harmony scheme, then refine. Start from a base color and a scheme — monochromatic/analogous (safe, low-contrast) or complementary (high-contrast, soften with tints/transitional tones) — then adjust saturation/brightness for interest.
- Neutral-dominant, accent-sparing — the 60-30-10 rule. ~60% primary/neutral surface, ~30% secondary, ~10% accent reserved for elements that must stand out (CTAs). Primary/secondary stay relatively neutral for readability.
- Define color by role, not just hue. A system needs: primary / secondary / tertiary accents (with on-color pairs), neutral/surface tones, and functional/semantic colors (success, warning, error, info).
- Keep functional colors distinct from brand. If the brand color is a bright red near the error red, define a separate error family so users don't confuse branding with feedback.
- Build tonal scales + tokens. Generate consistent tints/shades from each base; expose them as named variables/tokens (functional or numeric naming) so one change propagates and the system stays consistent.
- Never rely on color alone, and check every text/icon/surface pair for contrast — defer the actual ratios and color-blindness checks to the
accessibilityaudit, but the palette must be built to pass them.
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.
- 11d ago First seen · 104 lines · 82 tokens per session scan A a7b03a26325e
ux-color is a skill published in the GitHub repository Uxcel-Lab/product-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 82 tokens to every session and 1,915 once invoked, about $0.0004 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 2026-08-31.
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