Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
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.
[](https://agentmods.dev/skills/oaustegard/claude-skills/charting)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/charting"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/charting/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/oaustegard/claude-skills/charting"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/charting.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 | $0.00100 | $0.01514 |
| Opus 5 | $0.00050 | $0.00757 |
| Sonnet 5 | $0.00020 | $0.00303 |
| Haiku 4.5 | $0.00010 | $0.00151 |
Grade A, and why
charting 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 10d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Charting: Python Static Visualizations
Select the optimal Python charting library and produce clean, publication-quality output.
Library Selection Framework
Choose the library based on what the visualization represents, not habit.
Seaborn — DEFAULT for statistical/analytical charts
Seaborn wraps matplotlib with better defaults, tighter pandas integration, and fewer lines of code. Reach for seaborn first when the data lives in a DataFrame and the goal is analytical.
Use for: distributions (histograms, KDEs, violin plots, ECDFs), categorical comparisons (box plots, swarm plots, strip plots, bar plots), correlation (heatmaps, pair plots, regression plots), grouped/faceted views (FacetGrid, catplot, relplot).
Why: Automatic axis labeling from column names, coherent color palettes, built-in aggregation with confidence intervals, and hue/col/row faceting with minimal code.
Practical rule: If the code would call plt.bar(), plt.hist(), plt.scatter(), or build a heatmap with plt.imshow() — use the seaborn equivalent instead. It will look better with less effort.
Matplotlib — fine-grained control and non-standard layouts
Drop to raw matplotlib only when seaborn doesn't support the chart type or when pixel-level layout control is required.
Use for: custom multi-panel figures mixing chart types, unusual annotations (arrows, shaded regions, custom legends), non-standard axes (polar, broken axes, insets), animations, image overlays, or any layout where the default seaborn API is insufficient.
Combine with seaborn: Seaborn plots return matplotlib Axes objects. Apply matplotlib customization on top of seaborn output rather than rebuilding from scratch.
Graphviz — graph/network structures
Graphviz operates in a fundamentally different domain: nodes and edges, not x/y data.
Use for: dependency trees, flowcharts, state machines, org charts, entity-relationship diagrams, DAGs, call graphs, any directed or undirected graph structure.
What ships with it
1 file 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.
- 10d ago First seen · 131 lines · 100 tokens per session scan A 11d08da3e3be
charting is a skill published in the GitHub repository oaustegard/claude-skills (147 stars, last pushed yesterday), licensed MIT. It adds 100 tokens to every session and 1,514 once invoked, about $0.0005 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.
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