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/data-viz-audit)<a href="https://agentmods.dev/commands/adityaraj0421/naksha-studio/data-viz-audit"><img src="https://agentmods.dev/badge/commands/adityaraj0421/naksha-studio/data-viz-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 | $0.00045 | $0.01740 |
| Opus 5 | $0.00023 | $0.00870 |
| Sonnet 5 | $0.00009 | $0.00348 |
| Haiku 4.5 | $0.00005 | $0.00174 |
Grade A, and why
data-viz-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 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/data-viz-audit $ARGUMENTS
Audit a data visualization end-to-end: chart type selection, accessible color palette, annotations, anti-patterns (Phase 1 — always), and dashboard layout fit (Phase 2 — only when dashboard context is provided).
Usage: /data-viz-audit <chart description or code>
Examples:
/data-viz-audit pie chart showing 8 revenue channels — standalone, no dashboard/data-viz-audit monthly active users line chart — SaaS analytics dashboard with MRR, churn, active users KPI row/data-viz-audit <paste Chart.js config here> — e-commerce admin dashboard, secondary chart slot/data-viz-audit dual Y-axis line chart comparing ad spend and conversion rate
Process
1. Parse the Input
Extract from $ARGUMENTS:
- Chart input: pasted code (HTML, Chart.js config, SVG) or plain description
- Dashboard context: optional — surrounding layout description, screenshot reference, or explicit "standalone" note
- Chart type claimed vs. data relationship being shown
- Number of data series and whether data is continuous, discrete, or categorical
- Color variables in use (hex values or CSS custom properties)
2. Phase 1 — Chart Type Audit
Apply the chart type selection matrix from the Data Viz Designer reference:
| Data relationship | Correct chart |
|---|---|
| Trend over time (continuous) | Line chart |
| Trend over time (discrete periods) | Vertical bar |
| Part-to-whole (≤ 5 categories) | Donut |
| Part-to-whole (many categories) | Stacked bar |
| Comparison across categories | Grouped bar |
| Distribution | Histogram |
| Correlation | Scatter plot |
| Ranking with long labels | Horizontal bar |
| Hierarchical proportions | Treemap |
| Flow between stages | Sankey |
Anti-patterns — flag and reject:
- 3D charts of any kind → recommend flat equivalent
- Dual Y-axis → recommend two separate charts or indexed comparison
- Pie chart with > 5 slices → recommend horizontal bar chart
- Misleading truncated Y-axis → note and correct
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 · 167 lines · 45 tokens per session scan A 3be58d07b8fa
data-viz-audit is a command published in the GitHub repository Adityaraj0421/naksha-studio (316 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 1,740 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.
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.
site
Crawl a whole site and synthesize ONE canonical design system across all pages — unified tokens, coverage map, consistency grade.
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).