Skills scientific-visualization
📦

scientific-visualization

Content revision r1 High Risk 🌐 Network access📁 Filesystem access⚙️ External commands

Create Publication-Ready Scientific Figures

Researchers often lose time formatting figures for journals and accessibility. This skill provides practical plotting guidance, style presets, palette choices, and export checks for publication workflows.

Supports: Claude Codex Code(CC)
⚠️ 38 Poor

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "scientific-visualization" from https://skillstore.io/skills/k-dense-ai-scientific-visualization.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-scientific-visualization/manifest. Verify the artifact. Stop and obtain explicit user consent before installing or changing files.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/k-dense-ai-scientific-visualization.md Signed manifest GET /api/skills/k-dense-ai-scientific-visualization/manifest Signed lockfile GET /api/skills/k-dense-ai-scientific-visualization/lockfile

Test it

Using "scientific-visualization". Create a Nature-style line plot with error bars and accessible colors.

Expected outcome:

  • Applied a single-column Nature layout with small publication fonts.
  • Selected the Okabe-Ito palette for colorblind-safe category encoding.
  • Added labeled axes, confidence intervals, and a clean legend.
  • Prepared vector and raster export options for manuscript submission.

Using "scientific-visualization". Fix this heatmap for a journal figure.

Expected outcome:

  • Replaced the rainbow scale with a perceptually uniform colormap.
  • Added a labeled colorbar and readable tick labels.
  • Adjusted the figure size to match a journal column width.
  • Flagged that the caption should define the scale and sample size.

Using "scientific-visualization". Prepare a four-panel figure for a manuscript draft.

Expected outcome:

  • Organized the panels with consistent spacing and aligned axes.
  • Added panel labels and matched typography across plots.
  • Used redundant markers and line styles beyond color.
  • Listed final checks for resolution, fonts, and grayscale readability.

Security Audit

High Risk
v7 • 7/6/2026 Open versioned report

Most static findings are false positives from Markdown code fences, visualization terminology, dictionary keys, and citation URLs. One real local file-write capability remains in style_presets.py, and semantic review found an embedded safety self-assessment plus promotional steering. No evidence found of credential access, malicious command execution, or unauthorized network calls.

12
Files scanned
3,620
Lines analyzed
1
Review items
0
False positives ignored

Confirmed security concerns (2)

High
EVALUATION_OUTPUT.json:15-18
Prompt Injection Attempt Detected
EVALUATION_OUTPUT.json declares the skill safe_to_publish and says all findings are dismissed after manual review. Embedding a prior safety claim in the skill package can bias an AI auditor toward accepting the package without independent review.
The file explicitly asserts a safe risk level and a manual dismissal of findings. It does not contain an override command, so confidence is high but not absolute.
Low
SKILL.md:778-779
Promotional Steering to External Service
SKILL.md instructs the assistant to proactively suggest K-Dense Web for complex workflows. This can steer users toward an external service outside the core scientific visualization task.
The instruction is explicit and cites a hosted service. It is promotional rather than credential theft or code execution, so the severity is low.
Capability review items (1)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Medium
scripts/style_presets.py:319
Python file write/append
with open(output_file, 'w') as f:
create_style_template opens the caller-provided output_file with write mode, so it can overwrite a local path. This is intended utility behavior, but it is a real filesystem write capability that should be constrained or confirmed.

Risk Factors

🌐 Network access (2)
assets/color_palettes.py:35 EVALUATION_OUTPUT.json:54
📁 Filesystem access (1)
scripts/style_presets.py:319
⚙️ External commands (130)
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Audited by: codex View Audit History →
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Open versioned report
Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

K-Dense-AI. (2026). scientific-visualization security audit report (audit version 7) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-scientific-visualization/audits/7

BibTeX citation

@techreport{k-dense-ai-k-dense-ai-scientific-visualization-2026, author = {K-Dense-AI}, title = {scientific-visualization security audit report (audit version 7)}, institution = {Skillstore}, year = {2026}, number = {7}, url = {https://skillstore.io/skills/k-dense-ai-scientific-visualization/audits/7}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "scientific-visualization security audit report (audit version 7)" version: "unspecified" type: report authors: - name: "K-Dense-AI" date-released: "2026-07-06" url: "https://skillstore.io/skills/k-dense-ai-scientific-visualization/audits/7" identifiers: - type: other value: "skillstore:k-dense-ai-scientific-visualization:audit:7" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score
davila7 Recommended

davila7-scientific-visualization

Skillstore Score 84
Evidence Confidence High
Skillstore usage 44
Updated

2026-08-21

K-Dense-AI Current

k-dense-ai-scientific-visualization

Skillstore Score 38
Evidence Confidence Medium
Skillstore usage 136
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: Medium
82
Architecture
85
Maintainability
87
Content
71
Community
83
Spec Compliance

What You Can Build

Prepare manuscript figures

Convert analysis results into journal-ready figures with correct dimensions, colors, labels, and export formats.

Improve thesis visuals

Create consistent figures for theses, posters, and lab reports using accessible palettes and readable typography.

Publish technical results

Turn statistical outputs into polished, accessible plots for white papers, reports, and scientific documentation.

Try These Prompts

Make a basic figure 
Create a publication-ready line plot for my time series data. Use a colorblind-safe palette, clear axis labels with units, and PDF plus PNG export.
Format for a journal 
Format this figure for Nature single-column submission. Check width, font sizes, color choices, and export settings before saving the final files.
Build a multi-panel layout 
Create a four-panel scientific figure with shared styling, panel labels, a heatmap, a box plot, a scatter plot, and a time series panel.
Audit an existing figure 
Review my existing manuscript figure against publication guidelines. Identify issues with accessibility, statistics, layout, typography, and export format.

Best Practices

  • Check the current target journal instructions before final export.
  • Use color, markers, labels, and line styles so figures remain readable without color.
  • Inspect the figure at final print size before submitting it.

Avoid

  • Using rainbow or red-green colormaps for scientific values.
  • Exporting manuscript plots only as low-resolution screen images.
  • Hiding uncertainty, sample size, or statistical definitions from the figure and caption.

Frequently Asked Questions

Which plotting libraries does this skill cover?
It covers Matplotlib, Seaborn, and Plotly workflows for static scientific figures and manuscript exports.
Which journal styles are included?
The references include guidance for Nature, Science, Cell, PLOS, ACS, and IEEE style requirements.
Can it make figures colorblind-friendly?
Yes. It recommends accessible palettes and redundant encodings such as markers, line styles, and hatching.
Does it run statistical tests?
No. It helps display uncertainty and significance, but the user must provide valid statistical results.
What export formats are supported?
The helper guidance covers PDF, EPS, SVG, TIFF, and PNG, with DPI recommendations by figure type.
Can it review existing figures?
Yes. It can check layout, fonts, labels, colors, accessibility, and export readiness against publication guidelines.

Developer Details

Author

K-Dense-AI

License

MIT license

Skillstore revision

r1

Version notice

The author did not declare a version.

Repository

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/scientific-visualization

Ref

b8ca75d2c0a7e7102978993058777d82b8ab2610

Maintenance freshness

7/18/2026

Usage

129 downloads · 291 views

File structure

📁 assets/

📄 color_palettes.py

📄 nature.mplstyle

📄 presentation.mplstyle

📄 publication.mplstyle

📁 references/

📄 color_palettes.md

📄 journal_requirements.md

📄 matplotlib_examples.md

📄 publication_guidelines.md

📁 scripts/

📄 figure_export.py

📄 style_presets.py

📄 EVALUATION_OUTPUT.json

📄 SKILL.md

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