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AutoResearchClaw: Skill for Claude Code

.claude/skills/scientific-visualization/SKILL.md

scientific-visualization is a skill for Claude Code from aiming-lab/AutoResearchClaw. It costs 33 tokens per session (692 once invoked), scanned A, original, MIT.

Guidance for creating scientific charts and figures with Python's Matplotlib and Seaborn libraries. It covers readable layouts, journal dimensions, statistical annotations, and accessibility.

In plain words
What is it for?
It is for preparing publication-ready plots, multi-panel figures, color-safe charts, and graphics that meet print-resolution requirements.
Why use it?
It helps turn analysis results into figures that remain clear in research papers and for readers with color-vision differences.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is aiming-lab/AutoResearchClaw's own configuration. It tells Claude Code how to work on AutoResearchClaw itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything AutoResearchClaw configures →

not rated 15krepo +34 2mo ago A scan SkillSpector: pass 33 tokens original MIT
View source ↗ aiming-lab/AutoResearchClaw
About the project

AutoResearchClaw is a system that turns a research idea into a scientific paper through autonomous and collaborative AI research workflows. It is for researchers who want agents to investigate questions, run experiments, and produce papers, with optional human guidance. Catalogue skills and agents provide parts of its research workflow.

aiming-lab/AutoResearchClaw · 14,822 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to aiming-lab/AutoResearchClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/aiming-lab/AutoResearchClaw/main/.claude/skills/scientific-visualization/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw

Made for: Claude Code.

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.

agentmods badge for scientific-visualization

README.md
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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.

agentmods 80×15 button for scientific-visualization

Your own site · 80×15
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Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 692 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00033 $0.00692
Opus 5.5 $0.00013 $0.00277
Sonnet 5.5 $0.00007 $0.00138
Haiku 4.5 $0.00003 $0.00069

Measured 2mo ago against content hash 07ae48cf917a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

scientific-visualization 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 2mo 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.

.claude/skills/scientific-visualization/SKILL.md · 57 lines

How it starts

The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Scientific Visualization Best Practice

Figure Design Principles

  1. Every figure must have a clear, self-contained message
  2. Minimize chartjunk: remove gridlines, background shading, and 3D effects
  3. Use direct labeling instead of legends when possible
  4. Remove top and right spines for cleaner appearance
  5. Ensure all text is readable at final print size (minimum 6pt font)

Journal Figure Sizing

  1. Single column: 3.3-3.5 inches (85-89 mm) wide
  2. 1.5 column: 4.5-5.5 inches (114-140 mm) wide
  3. Double column / full width: 6.5-7.1 inches (165-180 mm) wide
  4. Resolution: 300 DPI minimum for raster; prefer vector formats (PDF, EPS, SVG)
  5. Check target journal author guidelines for exact specifications

Colorblind-Safe Design

  1. Use colorblind-friendly palettes: seaborn "colorblind", Okabe-Ito, viridis, cividis
  2. NEVER rely on color alone — combine with shape, pattern, or line style
  3. Avoid red-green combinations; prefer blue-orange or blue-yellow contrasts
  4. Test figures with a colorblind simulator before submission
  5. Ensure figures work in grayscale for print journals

Multi-Panel Layouts

  1. Label panels with uppercase letters: (A), (B), (C) in bold, top-left corner
  2. Use consistent axis scales across panels when comparing related data
  3. Share axes where appropriate to reduce redundancy
  4. Maintain consistent font sizes and line widths across all panels
  5. Use plt.subplots() with constrained_layout=True for automatic spacing

Statistical Annotations on Figures

  1. Show individual data points alongside summary statistics (box + strip plots)
  2. Always include error bars; specify type in caption (SEM, SD, 95% CI)
  3. Use significance brackets with stars: * p<.05, ** p<.01, *** p<.001
  4. Annotate effect sizes or key statistics directly on the figure when helpful
  5. Never use bar charts for small-n data — use dot plots or box plots instead

Export and Quality Checklist

  1. Save in vector format (PDF/SVG) for line art; TIFF/PNG for photographs
  2. Embed fonts or convert text to outlines for cross-platform consistency
  3. Verify axis labels include units in parentheses: "Time (s)", "Force (N)"
  4. Ensure figure caption fully explains all symbols, abbreviations, and panels
  5. Check that color-coded elements match between figure and caption

Read the full file on GitHub · 57 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. 2mo ago First seen · 57 lines · 33 tokens per session scan A 07ae48cf917a

Subscribe to this mod's changes

scientific-visualization is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,822 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 692 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 2026-08-30.

Analytics and metricsScientificautonomous-researchcitation-verificationllm-agentsmulti-agent-debateopenclawpaper-generation
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