plot

A tool that reads a data file and creates a chart by writing and running a Python script. It uses common Python chart libraries such as Matplotlib and Seaborn.

In plain words
What is it for?
Creating charts from CSV, TSV, Excel, or other data files, such as bar charts, line graphs, and scatter plots.
Why use it?
It removes the need to manually inspect the data, write plotting code, and save the resulting image. The chart can follow instructions about its type and appearance.

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/a554b554/reactant/plot
Any agent
npx skills add a554b554/Reactant --skill plot
Clone the repo
git clone --depth 1 https://github.com/a554b554/Reactant

Made for: Claude Code, Codex.

Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 792 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 $0.00020 $0.00792
Opus 5 $0.00010 $0.00396
Sonnet 5 $0.00004 $0.00158
Haiku 4.5 $0.00002 $0.00079

Measured 2d ago against content hash c41361fbe4b3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d 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/plot/SKILL.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Plot

Generate a data visualization by producing and executing a Python script (Matplotlib/Seaborn) based on the prompt, surrounding context, and a referenced data file.

Input

  • File path: the file being processed.
  • Surrounding text: the paragraph or block where the <@plot: ...> tag appears.
  • Prompt: description of the desired visualization, including chart type, styling, and any specific formatting instructions.

Workflow

  1. Read the surrounding text and prompt.
  2. Identify the data file from the prompt's context references ( ``). Read the data file to understand its structure (columns, types, number of rows).
  3. Identify the output path from the prompt's context references. If none is specified, save the plot in the same directory as the source file with a name derived from the prompt (e.g., plot_completion_time.png).
  4. Generate a self-contained Python script that:
    • Reads the data file (using pandas for CSV/TSV/Excel, or appropriate libraries for other formats).
    • Produces the requested visualization using matplotlib and/or seaborn.
    • Applies any styling or formatting instructions from the prompt.
    • Saves the figure to the output path with dpi=300 and bbox_inches='tight'.
  5. Execute the script in a single Bash command:
    python -c "
    <generated script here>
    "
    
  6. Verify the output file was created.
  7. Replace the <@plot: ...> tag with a format-appropriate image reference:
    • Markdown (.md): ![description](output_path.png)
    • LaTeX (.tex): \includegraphics{output_path.png}
    • other format, try to determine by yourself.

Context References

The prompt may contain context references wrapped in double backticks ( ``). These point to external resources.

  • As data source: The reference points to the data file to visualize. E.g., <@plot: bar chart using ``data/results.csv``> -- read data/results.csv as the input data.
  • As output path: The reference specifies where to save the generated plot. E.g., <@plot: line chart, save to ``figures/trend.png``> -- save the plot to figures/trend.png.
  • Resolve references relative to the source file's directory unless an absolute path is given.
  • Remove the delimiters from the output.
  • If the input tag does not contain any context reference, ask user the elaborate.

Read the full file on GitHub · 60 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. 2d ago First seen · 60 lines · 20 tokens per session scan A c41361fbe4b3

Subscribe to this mod's changes

plot is a skill published in the GitHub repository a554b554/Reactant (54 stars, last pushed 29d ago), licensed MIT. It adds 20 tokens to every session and 792 once invoked, about $0.0001 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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