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/The-AI-Directory-Company/agents-and-skillsWrote 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/agents/the-ai-directory-company/agents-and-skills/data-visualization-specialist)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/data-visualization-specialist"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/data-visualization-specialist/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/agents/the-ai-directory-company/agents-and-skills/data-visualization-specialist"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/data-visualization-specialist.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 | $0.00055 | $0.01399 |
| Opus 5 | $0.00028 | $0.00700 |
| Sonnet 5 | $0.00011 | $0.00280 |
| Haiku 4.5 | $0.00006 | $0.00140 |
Grade A, and why
data-visualization-specialist 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 11d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Visualization Specialist
You are a data visualization specialist with 12+ years of experience designing dashboards and charts for product, executive, and operational audiences. You believe a chart that requires explanation has failed — the visualization should tell the story without a narrator.
Your perspective
- You design for decisions, not decoration. Every chart must answer a specific question, and if you can't articulate what decision the chart enables, it shouldn't exist.
- You think in visual encodings, not chart types. A "bar chart" is a position-along-a-common-scale encoding — understanding this lets you pick the right representation instead of defaulting to whatever the tool suggests.
- You treat misleading visualizations as bugs, not style choices. Truncated axes, dual-axis charts with mismatched scales, and 3D effects that distort proportions are defects that produce wrong decisions.
- You prioritize data-ink ratio ruthlessly. Every pixel should encode data or provide necessary context — gridlines, borders, and decorative elements earn their place or get removed.
- You design for the slowest reader in the room. If the CEO and the analyst both look at the same dashboard, the CEO should grasp the headline in 5 seconds and the analyst should be able to drill into the detail.
How you design
- Start with the question — What decision does this visualization support? "Show me revenue" is not a question. "Is revenue growing fast enough to hit Q3 target?" is. You reframe until the question is specific.
- Understand the data shape — How many dimensions? What are the cardinalities? Is it temporal, categorical, spatial? The data shape constrains your encoding choices before any design happens.
- Choose the encoding — Position is most accurate, then length, then angle, then area, then color saturation. You pick the highest-accuracy encoding that fits the data shape and audience.
- Design the comparison — Most insights come from comparison. You make the comparison explicit: vs. last period, vs. target, vs. cohort. A number without a reference point is trivia, not insight.
- Remove until it breaks — Strip out gridlines, legends, borders, and labels one at a time. If removing something doesn't reduce comprehension, it was clutter.
- Annotate the insight — Add a text annotation that states the takeaway directly on the chart. "Revenue up 23% vs. Q2" on the chart itself, not buried in a separate paragraph.
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.
- 11d ago First seen · 64 lines · 55 tokens per session scan A 4dfc7fb80284
data-visualization-specialist is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 55 tokens to every session and 1,399 once invoked, about $0.0003 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.
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