Scientific Visualization Tools
SkillDatabases & dataScientific visualization workflow guide for publication-ready static figures with seaborn or matplotlib and interactive figures with Plotly. Use when the user asks for scientific plots, cohort or assay figures, publication graphics, dashboards, or reusable plotting scripts for research datasets.
Use Scientific Visualization Tools in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Scientific Visualization Tools and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the Scientific Visualization Tools skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by drugclaw/drugclaw in skills/science/scientific-visualization-tools/SKILL.md and read by ahel’s review.
Use this skill when the user needs a figure artifact rather than only a numeric summary.
Typical triggers:
- publication-ready scatter, box, violin, bar, or heatmap figures
- interactive HTML charts for exploratory research data review
- small reusable plotting scripts for assay, omics, or cohort tables
- consistent styling across scientific plots
Environment Check
which python3 || true
python3 - <<'PY'
mods = ["pandas", "matplotlib", "seaborn", "plotly"]
for name in mods:
try:
__import__(name)
print(f"{name}: ok")
except Exception as exc:
print(f"{name}: missing ({exc})")
PY
If key plotting modules are missing, recommend the optional drug-sandbox image documented in docs/operations/science-runtime.md.
Bundled Assets
templates/publication_plot.pytemplates/interactive_plot.py
Preferred Workflow
- Decide first whether the output should be static publication art or interactive exploration.
- Keep the plotting script parameterized by column names rather than hardcoding one dataset.
- Save the figure and a small JSON summary of what was plotted.
- Do not use interactive charts where a paper-ready static figure is required.
- Do not claim statistical meaning from a plot unless the underlying analysis is also reported.
Static Publication Plots
python3 templates/publication_plot.py \
--input figures/assay.csv \
--kind box \
--x-column arm \
--y-column response \
--color-column arm \
--output figures/assay_box.png \
--summary figures/assay_box.json
Supported baseline kinds:
scatterlineboxviolinbarheatmap
Interactive Plotly Charts
python3 templates/interactive_plot.py \
--input figures/cohort.csv \
--kind scatter \
--x-column age \
--y-column biomarker \
--color-column response \
--output figures/cohort_scatter.html \
--summary figures/cohort_scatter.json
Use this for exploratory review, dashboards, and lightweight sharing.
Related Skills
For statistical inference behind a plot, activate stat-modeling-tools.
For Kaplan-Meier and time-to-event figures, activate survival-analysis-tools.
For broader scientific-writing and manuscript-structure work, activate scientific-workflow-tools.
Signals
- GitHub stars
- 125
- Forks
- 9
- Last commit
- Mar 2026
Advanced
- Item type
- skill
- Key
scientific-visualization-tools- Source
- github.com/drugclaw/drugclaw
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