Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Adityaraj0421/naksha-studio/plugin install naksha-studioWrote 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/commands/adityaraj0421/naksha-studio/brand-kit)<a href="https://agentmods.dev/commands/adityaraj0421/naksha-studio/brand-kit"><img src="https://agentmods.dev/badge/commands/adityaraj0421/naksha-studio/brand-kit.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 | $0.00033 | $0.03082 |
| Opus 5 | $0.00016 | $0.01541 |
| Sonnet 5 | $0.00007 | $0.00616 |
| Haiku 4.5 | $0.00003 | $0.00308 |
Grade A, and why
brand-kit 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 8d 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 — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/brand-kit
You are generating a complete brand kit from minimal inputs (1-2 colors and optionally a brand name/mood). The output includes a color system, typography scale, spacing system, component tokens, and optionally Figma styles.
Input: $ARGUMENTS
Read ${CLAUDE_PLUGIN_ROOT}/skills/design/references/design-system-lead.md for token architecture and ${CLAUDE_PLUGIN_ROOT}/skills/design/references/ui-designer.md for visual design principles.
Process
0. Load Project Context (v5)
Before parsing inputs, check for .naksha/project.json (search up to 3 directory levels):
If found, read v5 constraints to inform generation:
constraints.grid→ use as the base unit for all generated spacing tokens. If"8px", every spacing value is a multiple of 8.constraints.dark_mode→ iffalse, omit dark mode token variants. Iftrue, dark mode is required. If absent, include as optional.constraints.accessibility_target→ if"WCAG AAA", ensure all generated color combinations achieve ≥ 7:1 contrast. Default to WCAG AA (4.5:1).constraints.out_of_scope→ if an excluded item would normally be part of the brand kit (e.g., dark mode tokens, icon set), skip it silently and note the exclusion.component_patterns→ if named patterns exist, ensure the generated component tokens (button, card, input radii/padding) match the recorded descriptions. Note any intentional divergences.
If v5 constraints conflict with the user's explicit request (e.g., user asks for dark mode but dark_mode: false): generate what the user asked for, but add a note: "⚠ Note: project memory has dark_mode: false. Update via /naksha-remember if this decision has changed."
If no project found: continue with defaults.
After generating, emit memory update blocks for constraints implied by brand decisions:
- Mood
"professional"or"minimal"with no existingaccessibility_target→ emit:constraints.accessibility_target = "WCAG AA" - Spacing system generated with a specific base unit → if no
constraints.gridexists, emit:constraints.grid = "{base unit}"
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.
- 8d ago First seen · 346 lines · 33 tokens per session scan A 2486cf9e5e23
brand-kit is a command published in the GitHub repository Adityaraj0421/naksha-studio (316 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 3,082 once invoked, about $0.0002 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 2026-08-30.
Other commands, from other repositories
brand
Generate a full editorial brand-guidelines book for any URL. 14 chapters covering brand essence/archetype, colour, typography, spacing, shape, iconography, motion, components, voice, accessibility, tokens, and how-to-use guidance. Print-ready, dark-mode toggle, hand-off-ready single HTML.
theme-swap
Recolour an extracted site's design around a new brand primary. OKLCH hue rotation preserves perceptual lightness — neutrals, type, spacing, and motion stay untouched. Side-by-side HTML preview + recoloured tokens (DTCG, Tailwind, shadcn, Figma).
dna
Place a design in the measured design space — nearest systems, per-axis percentiles, outliers.
extract
Extract the complete design language from a URL — DTCG tokens, Tailwind, Figma vars, motion, voice, components.
fidelity
Measure how faithfully a clone reproduces a site — pixel-diff plus motion-fidelity into one 0-100 score, a letter grade, a ranked correction plan, and a shareable card.
pack
Bundle every designlang output (DTCG tokens, Tailwind, shadcn, Figma vars, motion, anatomy, Storybook, prompts) into one polished design-system directory ready to zip and ship.