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 luccapinto/agentic-data-kit --skill applying-visual-identitygit clone --depth 1 https://github.com/luccapinto/agentic-data-kitWrote 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/luccapinto/agentic-data-kit/applying-visual-identity)<a href="https://agentmods.dev/skills/luccapinto/agentic-data-kit/applying-visual-identity"><img src="https://agentmods.dev/badge/skills/luccapinto/agentic-data-kit/applying-visual-identity/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/luccapinto/agentic-data-kit/applying-visual-identity"><img src="https://agentmods.dev/badge/skills/luccapinto/agentic-data-kit/applying-visual-identity.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 | $0.00103 | $0.00699 |
| Opus 5.5 | $0.00041 | $0.00280 |
| Sonnet 5.5 | $0.00021 | $0.00140 |
| Haiku 4.5 | $0.00010 | $0.00070 |
Grade A, and why
applying-visual-identity 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 1mo 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: applying-visual-identity
A company's visual identity lives in a DESIGN.md at the project root — the open
spec from Google Labs (Apache-2.0, 2026). It pairs machine-readable design tokens (YAML
frontmatter) with human-readable rationale (prose), so any AI tool renders the brand the same
way across sessions. This skill reads that file and applies it; if none exists, it scaffolds one.
Workflow
- Locate. Look for
DESIGN.mdin the project root. If found, parse the frontmatter tokens — they are the normative values; the prose is guidance. - Apply. Map tokens to the target medium:
- HTML/reveal.js/Marp → CSS custom properties on
:root(--brand-primary,--font-heading, …). - Markdown/docs → headings, callouts, accent usage per the Do's/Don'ts.
- Resolve token references like
{colors.primary}before output.
- HTML/reveal.js/Marp → CSS custom properties on
- Stay in-spec. Use only defined tokens; don't introduce off-palette colors or fonts. If a needed token is missing, ask or pick the closest defined one and note it.
- Scaffold when absent. If there's no
DESIGN.md, offer to create one fromtemplates/DESIGN.md— ask for (or extract from a provided logo/site) the primary color, font, and tone. Never silently invent a brand; fall back to a neutral theme until confirmed.
The DESIGN.md format (Google Labs spec)
YAML frontmatter holds the tokens; the body documents intent in fixed sections.
Frontmatter token groups: colors, typography (objects: fontFamily, fontSize,
fontWeight, lineHeight, letterSpacing), spacing, rounded, shadows, components
(per-component token refs). Colors accept hex / rgb / hsl / oklch(). Dimensions carry units
(px, rem). References use braces: {colors.primary}, {typography.h1}.
Body sections (in order): Overview · Colors · Typography · Layout & Spacing · Elevation & Depth · Shapes · Components · Do's and Don'ts.
See templates/DESIGN.md for a complete, fillable example.
What ships with it
1 file 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.
- 1mo ago First seen · 44 lines · 0 tokens per session scan A 471a8fcf7389
applying-visual-identity is a skill published in the GitHub repository luccapinto/agentic-data-kit (7 stars, last pushed 3mo ago), licensed MIT. It adds 103 tokens to every session and 699 once invoked, about $0.0004 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 2026-08-31.
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