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.
git clone --depth 1 https://github.com/Adityaraj0421/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/competitive-audit)<a href="https://agentmods.dev/commands/adityaraj0421/naksha-studio/competitive-audit"><img src="https://agentmods.dev/badge/commands/adityaraj0421/naksha-studio/competitive-audit.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 | $0.00026 | $0.01312 |
| Opus 5 | $0.00013 | $0.00656 |
| Sonnet 5 | $0.00005 | $0.00262 |
| Haiku 4.5 | $0.00003 | $0.00131 |
Grade A, and why
competitive-audit 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 7d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/competitive-audit
You are running a competitive design audit. Capture a competitor's website and extract actionable design intelligence across 8 categories.
Input: $ARGUMENTS
Step 1 — Parse URL
Extract the URL from $ARGUMENTS.
- If a bare domain is given (e.g.,
stripe.com), prependhttps://. - If no URL is given, ask: "Please provide a competitor URL (e.g.,
stripe.com)."
Set TARGET_URL to the resolved URL.
Step 2 — Capture Sequence
Run all three captures in order.
Desktop Capture
- Resize browser to 1440×900.
- Navigate to
TARGET_URL. - Take a screenshot — label it
desktop-above-fold. - Take an accessibility snapshot to extract text, roles, and structure.
Mid-Page Capture
- Keep the browser at 1440×900.
- Navigate to
TARGET_URL#main-contentto simulate a scroll (best-effort).- If the anchor doesn't exist, note "first viewport only" and skip this screenshot.
- Take a screenshot — label it
desktop-mid-page.
Mobile Capture
- Resize browser to 390×844.
- Navigate to
TARGET_URL. - Take a screenshot — label it
mobile-above-fold.
Step 3 — Extract Design Intelligence
Analyze all screenshots and the accessibility snapshot. Extract findings across all 8 categories below. Use the snapshot text to extract actual values where possible (font names, hex codes in inline styles, etc.).
1. Color System
Extract primary, secondary, accent, background, and text colors. Report actual hex codes if visible in the snapshot or source. If not extractable, describe visually (e.g., "deep navy blue", "warm off-white").
2. Typography
Identify heading font family, size, weight, and body font family, size. Note if a variable font or system font stack is used. Assess line-height style (tight / normal / loose).
3. Layout Grid
Estimate column count, max-width (px), gutter approach (tight/spacious), and how the layout shifts at mobile breakpoint.
4. Navigation
Identify the top-nav pattern: transparent/solid/sticky/minimal. Note logo placement (left/center), primary CTA style in nav, and mobile nav pattern (hamburger/drawer/tabs).
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.
- 7d ago First seen · 149 lines · 26 tokens per session scan A 4662e241429c
competitive-audit is a command published in the GitHub repository Adityaraj0421/naksha-studio (316 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 1,312 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 2026-08-30.
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.