data-visualization

data-visualization is a skill for Claude Code, Codex from medy-gribkov/arcana. It costs 35 tokens per session (3,840 once invoked), scanned A, original, Apache-2.0.

Guidance for presenting data as readable, responsive charts using Recharts, Chart.js, or D3.js. It covers choosing chart types, supporting different screen sizes, and using colors that remain distinguishable for people with color-vision deficiencies.

In plain words
What is it for?
Use it to choose between bar charts, line charts, scatter plots, and other chart types, build them in a web interface, handle large datasets, and improve accessibility.
Why use it?
A chart can make data harder to understand when its form does not match the information. This helps avoid misleading or difficult-to-read visualizations.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/medy-gribkov/arcana/data-visualization
Any agent
npx skills add medy-gribkov/arcana --skill data-visualization
Clone the repo
git clone --depth 1 https://github.com/medy-gribkov/arcana

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin data-visualization/plugin install data-visualization after adding the marketplace above.

Wrote 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.

agentmods badge for data-visualization

README.md
[![agentmods](https://agentmods.dev/badge/skills/medy-gribkov/arcana/data-visualization.svg)](https://agentmods.dev/skills/medy-gribkov/arcana/data-visualization)
Your own site
<a href="https://agentmods.dev/skills/medy-gribkov/arcana/data-visualization"><img src="https://agentmods.dev/badge/skills/medy-gribkov/arcana/data-visualization.svg" alt="Measured on agentmods" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,840 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00035 $0.03840
Opus 5 $0.00017 $0.01920
Sonnet 5 $0.00007 $0.00768
Haiku 4.5 $0.00003 $0.00384

Measured 4d ago against content hash eb78a971c170, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data-visualization 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 4d 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.

skills/data-visualization/SKILL.md · 509 lines

How it starts

The opening of the file, as written. The whole thing — 509 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Visualization

Build accessible, performant data visualizations using modern libraries. Choose the right chart type, implement responsive layouts, and optimize for large datasets.

Chart Type Selection

Choose charts based on data relationships, not aesthetics.

BAD - Wrong chart for the task:

// Pie chart for comparing 12 categories - hard to compare angles
<PieChart width={400} height={400}>
  <Pie data={monthlyData} dataKey="value" nameKey="month" />
</PieChart>

// Line chart for categorical data with no time relationship
<LineChart data={productCategories}>
  <Line dataKey="sales" />
</LineChart>

GOOD - Chart matches data structure:

// Bar chart for category comparison - easy to compare lengths
<BarChart width={600} height={400} data={monthlyData}>
  <CartesianGrid strokeDasharray="3 3" />
  <XAxis dataKey="month" />
  <YAxis />
  <Tooltip />
  <Bar dataKey="value" fill="#d4943a" />
</BarChart>

// Scatter plot for correlation analysis
<ScatterChart width={600} height={400}>
  <CartesianGrid />
  <XAxis dataKey="age" name="Age" />
  <YAxis dataKey="salary" name="Salary" />
  <Scatter data={employees} fill="#d4943a" />
</ScatterChart>

Chart selection guide:

  • Bar/Column: Compare categories, rankings, discrete values
  • Line: Time series, trends over continuous periods
  • Scatter: Correlation between two variables, clustering
  • Heatmap: Patterns in 2D categorical data (day/hour traffic)
  • Area: Cumulative values over time, part-to-whole relationships
  • Avoid pie charts: Use bar charts instead (easier comparison)

Recharts Implementation

Recharts provides React-native declarative charts with built-in responsiveness.

BAD - Fixed dimensions, no accessibility:

function SalesChart({ data }) {
  return (
    <LineChart width={800} height={300} data={data}>
      <Line dataKey="sales" stroke="red" />
      <Line dataKey="profit" stroke="green" />
    </LineChart>
  );
}

GOOD - Responsive, accessible, properly labeled:

import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer } from 'recharts';

interface DataPoint {
  date: string;
  sales: number;
  profit: number;
}

interface SalesChartProps {
  data: DataPoint[];
}

const SalesChart: React.FC<SalesChartProps> = ({ data }) => {
  return (
    <ResponsiveContainer width="100%" height={400}>
      <LineChart
        data={data}
        margin={{ top: 5, right: 30, left: 20, bottom: 5 }}
        aria-label="Sales and profit trend over time"
      >
        <CartesianGrid strokeDasharray="3 3" stroke="#333" />
        <XAxis
          dataKey="date"
          stroke="#666"
          tick={{ fill: '#666' }}
        />
        <YAxis
          stroke="#666"
          tick={{ fill: '#666' }}
          label={{ value: 'USD', angle: -90, position: 'insideLeft' }}
        />
        <Tooltip
          contentStyle={{ backgroundColor: '#1a1a1a', border: '1px solid #333' }}
          formatter={(value: number) => `$${value.toLocaleString()}`}
        />
        <Legend />
        <Line
          type="monotone"
          dataKey="sales"
          stroke="#3b82f6"
          strokeWidth={2}
          dot={{ fill: '#3b82f6', r: 4 }}
          activeDot={{ r: 6 }}
          name="Sales"
        />
        <Line
          type="monotone"
          dataKey="profit"
          stroke="#d4943a"
          strokeWidth={2}
          dot={{ fill: '#d4943a', r: 4 }}
          name="Profit"
        />
      </LineChart>
    </ResponsiveContainer>
  );
};

Read the full file on GitHub · 509 lines

Changes

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.

  1. 4d ago First seen · 509 lines · 35 tokens per session scan A eb78a971c170

Subscribe to this mod's changes

data-visualization is a skill published in the GitHub repository medy-gribkov/arcana (1 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 3,840 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 2026-08-31.

Related

Other skills, from other repositories

bailian-train-deploy

用百炼 CLI (bl) 走完"数据→微调训练→导出→部署→调用"的完整闭环,或跳过训练直接部署基座模型。支持文本模型(SFT/DPO/CPT)、音频 TTS 模型(CosyVoice)、图像生成模型(Wan2.7)和视频生成模型(Wan i2v/kf2v)微调。涵盖数据集校验/上传、创建微调任务、等待训练、导出最佳 checkpoint、创建推理部署、等待就绪、给出调用示例。当用户提到在百炼 / DashScope / 阿里云模型工作室上"训练模型""微调""fine-tune""finetune""部署模型""模型上线""把微调模型跑起来/调用""训练一个推理模型""继续预训练""LoRA/SFT/DPO…

modelstudioai/skills · 321 tokens

cre-asset-management

CRE Asset Management analysis suite — 9 specialist skills for post-acquisition multifamily operations including annual budgeting, monthly variance analysis, rent collection, renewal decisions, lease-up tracking, capex execution, NOI improvement, hold/sell/refi scenario analysis, and quarterly asset review memos.

ahacker-1/cre-agent-skills · 62 tokens

cre-retail

CRE Retail analysis suite - 8 specialist skills for U.S. retail trade-area studies, rent roll and tenant mix, lease abstraction, co-tenancy and anchor risk, CAM recovery, underwriting, financing fit, and investment committee memo writing.

ahacker-1/cre-agent-skills · 52 tokens

cre-closing

CRE Closing management suite — 2 specialist skills for closing checklist coordination and funds flow preparation for multifamily acquisitions.

ahacker-1/cre-agent-skills · 26 tokens

cre-financing

CRE Financing analysis suite — 3 specialist skills for lender identification, quote comparison, and term sheet assembly for multifamily acquisition debt sourcing.

ahacker-1/cre-agent-skills · 31 tokens

aspireify

../../../../../skills/aspireify/SKILL.md.

microsoft/aspire-skills · 0 tokens