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.
npx skills add JayRHa/AgentSkills --skill chart-choosergit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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/skills/jayrha/agentskills/chart-chooser)<a href="https://agentmods.dev/skills/jayrha/agentskills/chart-chooser"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/chart-chooser/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/skills/jayrha/agentskills/chart-chooser"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/chart-chooser.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 | $0.00135 | $0.01461 |
| Opus 5.5 | $0.00054 | $0.00584 |
| Sonnet 5.5 | $0.00027 | $0.00292 |
| Haiku 4.5 | $0.00014 | $0.00146 |
Grade A, and why
chart-chooser 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 1mo 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chart Chooser
Overview
This skill turns a dataset plus an analytical question into the right chart, and ensures the result is clean, accurate, and not misleading. It covers two jobs:
- Choose — map the question intent and data shape to a chart type.
- Polish — apply encoding, labeling, axis, color, and honesty rules so the chart reads correctly at a glance.
Keywords: data visualization, chart type, graph selection, bar chart, line chart, scatter plot, histogram, heatmap, pie chart, dashboard, axis truncation, misleading charts, color accessibility, data-ink, encoding.
Use this skill whenever someone needs to decide how to show data, or wants an existing visual critiqued.
Workflow
Follow these steps in order. Do not jump to a chart type before clarifying intent and data shape.
- Identify the question intent. Pick the dominant one (a chart should answer one question well):
- Comparison (which is bigger/smaller across categories)
- Trend / change over time
- Distribution (shape, spread, outliers of one variable)
- Relationship / correlation (two+ numeric variables)
- Part-to-whole (composition of a total)
- Ranking (ordered comparison)
- Geospatial (values across places)
- Flow / part-to-whole over stages
- Profile the data shape. Note for each variable: type (categorical / ordinal / quantitative / temporal / geographic), cardinality (number of distinct values), and whether values can be summed to a meaningful total (needed for part-to-whole).
- Match intent + shape to a chart. Use the decision table in
references/chart-decision-matrix.md. Resolve ties with the "pick the simplest that answers the question" rule. - Choose encodings. Assign data fields to position, length, color, and size using the accuracy ranking in Best Practices. Position/length beat color/area for quantitative accuracy.
- Apply honesty + clarity rules. Run the checklist in
references/clarity-and-honesty-checklist.md: axis baselines, sorting, direct labels, color accessibility, decluttering. - Produce the output. Either describe the spec (chart type, x, y, color, sort, annotations) or generate code. Use
scripts/suggest_chart.pyto get a programmatic recommendation from a quick data profile. - Self-review. Verify the chart answers the original question in under 5 seconds and contains no misleading element.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 82 lines · 135 tokens per session scan A 97e223be0607
chart-chooser is a skill published in the GitHub repository JayRHa/AgentSkills (12 stars, last pushed 2mo ago), licensed MIT. It adds 135 tokens to every session and 1,461 once invoked, about $0.0005 per session on Opus 5.5. 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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