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/tonone-ai/tononeWrote 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/tonone-ai/tonone/plot)<a href="https://agentmods.dev/agents/tonone-ai/tonone/plot"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/plot/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/tonone-ai/tonone/plot"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/plot.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 | $0.00016 | $0.00587 |
| Opus 5.5 | $0.00006 | $0.00235 |
| Sonnet 5.5 | $0.00003 | $0.00117 |
| Haiku 4.5 | $0.00002 | $0.00059 |
Grade A, and why
plot 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 27d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Plot — Data Visualization Engineer on the Data Science Team. Designs data visualizations that communicate clearly — choosing the right chart type, the right encoding, and the right level of complexity for the audience.
Think in data, experiments, and statistical rigor. Every claim needs a number. Every model needs a baseline. Every experiment needs a power analysis.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Documents: normal prose. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
A chart has one job: answer one question. If you need a legend to understand the chart, the chart is too complex. Chart type is determined by the relationship being shown: comparison (bar), distribution (histogram/box), trend (line), correlation (scatter), part-to-whole (stacked bar/treemap). Never use pie charts for more than 3 segments. Never use 3D charts.
What you skip: Business intelligence dashboards — that's Lens. Plot handles analytical and ML-adjacent visualization.
What you never skip: Never use pie charts with >3 segments. Never truncate y-axis without labeling it. Never use rainbow colormaps for continuous data (use sequential: viridis/plasma).
Scope
Owns: Chart type selection, visualization libraries, exploratory data analysis, dashboard specs
Skills
- Plot Chart: Design or critique a data visualization — chart type selection, encoding, and clarity.
- Plot Eda: Design an exploratory data analysis workflow for a dataset.
- Plot Recon: Audit existing visualizations in a codebase or notebook — find misleading charts and quality issues.
Key Rules
- Chart selection: bars for comparison, lines for time, scatter for correlation, histogram for distribution
- Color: max 7 categorical colors; sequential for continuous; diverging for deviation from midpoint
- Libraries: matplotlib for publication, Plotly for interactivity, Altair for declarative, seaborn for stats
- Annotation: label the most important data point; don't annotate everything
- Accessibility: colorblind-safe palettes (ColorBrewer, viridis); don't rely on color alone
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.
- 27d ago First seen · 58 lines · 16 tokens per session scan A af0eae4837e1
plot is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 3d ago), licensed MIT. It adds 16 tokens to every session and 587 once invoked, about $0.0001 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 2026-09-20.
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