Skills nature-figure
📦

nature-figure

v2.0.0 Content revision r1 Safe 📁 Filesystem access⚙️ External commands

Create Journal-Ready Scientific Figures

Scientific figures often fail because story, evidence, export, and review checks are separated. This skill guides Claude, Codex, and Claude Code through one publication-ready workflow.

Supports: Claude Codex Code(CC)
🥇 85 Gold

Install with my Agent

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

Agent request
Review the Skillstore skill "nature-figure" from https://skillstore.io/skills/yuan1z0825-nature-figure.md and its manifest at https://skillstore.io/api/skills/yuan1z0825-nature-figure/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

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/yuan1z0825-nature-figure.md Signed manifest GET /api/skills/yuan1z0825-nature-figure/manifest Signed lockfile GET /api/skills/yuan1z0825-nature-figure/lockfile

Test it

Using "nature-figure". A researcher asks for a Python figure showing tumor response, histology, and safety markers.

Expected outcome:

  • A figure contract that states the core claim and evidence order.
  • A panel plan with a dominant efficacy panel and supporting safety panels.
  • A Python plotting workflow with editable vector export and source-data notes.

Using "nature-figure". A student shares an R heatmap figure and asks why it looks crowded.

Expected outcome:

  • A prioritized review of typography, annotation density, color scale, and legend placement.
  • Concrete revision steps for patchwork or ComplexHeatmap output.
  • A final export checklist for SVG, PDF, TIFF, and preview files.

Using "nature-figure". A team wants a Nature-style method comparison figure from existing benchmark tables.

Expected outcome:

A backend-gated plan with comparison bars, interval plots, statistical labels, export settings, and checks against misleading visual emphasis.

Security Audit

Safe
v5 • 7/21/2026 Open versioned report

All 101 static detections are false positives after contextual review. They match local figure-output directory creation, documentation links, plot labels, dictionary keys, or formatting guidance rather than command execution, credential access, traversal, or reconnaissance. No prompt injection, data-exfiltration intent, or other semantic security finding was evidenced.

45
Files scanned
6,373
Lines analyzed
0
Review items
0
False positives ignored

Risk Factors

📁 Filesystem access (47)
assets/figures4papers/figure_brainteaser/plot_brute_force.py:246 assets/figures4papers/figure_brainteaser/plot_correctness_by_category.py:130 assets/figures4papers/figure_brainteaser/plot_correctness_by_subcategory.py:129 assets/figures4papers/figure_brainteaser/plot_rewriting.py:103 assets/figures4papers/figure_brainteaser/plot_selfcorrection_math.py:97 assets/figures4papers/figure_CellSpliceNet/plot_ablation.py:84 assets/figures4papers/figure_CellSpliceNet/plot_comparison.py:107 assets/figures4papers/figure_Cflows/plot_comparison_Ablation.py:61 assets/figures4papers/figure_Cflows/plot_comparison_GeneRegulatory.py:71 assets/figures4papers/figure_Cflows/plot_comparison_Trajectory.py:71 assets/figures4papers/figure_Dispersion/plot_idea.py:74 assets/figures4papers/figure_Dispersion/plot_illustration.py:402 assets/figures4papers/figure_FPGM/plot_freq_prior.py:145 assets/figures4papers/figure_ImmunoStruct/plot_bars.py:69 assets/figures4papers/figure_ImmunoStruct/plot_bars.py:123 assets/figures4papers/figure_ImmunoStruct/plot_bars.py:168 assets/figures4papers/figure_ImmunoStruct/plot_bars.py:214 assets/figures4papers/figure_ophthal_review/plot_composition.py:41 assets/figures4papers/figure_ophthal_review/plot_trend.py:83 assets/figures4papers/figure_RNAGenScape/plot_comparison.py:110 assets/figures4papers/figure_RNAGenScape/plot_comparison.py:226 assets/figures4papers/figure_RNAGenScape/plot_hole_manifold.py:81 assets/figures4papers/figure_RNAGenScape/plot_manifold.py:60 assets/figures4papers/figure_RNAGenScape/plot_sweep.py:76 assets/figures4papers/figure_VIGIL/plot_comparison_radar.py:160 assets/figures4papers/figure_VIGIL/plot_posttraining.py:69 references/api.md:389 references/backend-selection.md:71 references/chart-types.md:277 references/common-patterns.md:344 references/demos.md:7 references/demos.md:25 references/demos.md:26 references/demos.md:27 references/demos.md:28 references/demos.md:29 references/demos.md:30 references/demos.md:31 references/demos.md:32 references/demos.md:33 references/demos.md:34 references/design-theory.md:334 references/tutorials.md:247 references/tutorials.md:70 references/tutorials.md:113 references/tutorials.md:166 references/tutorials.md:238
⚙️ External commands (19)
assets/figures4papers/figure_FPGM/plot_freq_prior.py:65 assets/figures4papers/figure_FPGM/plot_freq_prior.py:73 assets/figures4papers/figure_FPGM/plot_freq_prior.py:91 assets/figures4papers/figure_FPGM/plot_freq_prior.py:128 assets/figures4papers/figure_FPGM/plot_freq_prior.py:130 assets/figures4papers/figure_ophthal_review/plot_trend.py:17 SKILL.md:13 SKILL.md:14 SKILL.md:24 SKILL.md:26 SKILL.md:30 SKILL.md:32 SKILL.md:33 SKILL.md:35 SKILL.md:39 SKILL.md:45 SKILL.md:46 SKILL.md:53 SKILL.md:60
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: claude View Audit History →
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Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

Community contribution, refactored into static/dynamic layers. (2026). nature-figure security audit report (audit version 5) [Author version 2.0.0]. Skillstore. https://skillstore.io/skills/yuan1z0825-nature-figure/audits/5

BibTeX citation

@techreport{community-contribution-refactored-into-static-dynamic-layers-yuan1z0825-nature-figure-2026, author = {Community contribution, refactored into static/dynamic layers}, title = {nature-figure security audit report (audit version 5)}, institution = {Skillstore}, year = {2026}, number = {5}, url = {https://skillstore.io/skills/yuan1z0825-nature-figure/audits/5}, note = {Author version 2.0.0} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "nature-figure security audit report (audit version 5)" version: "2.0.0" type: report authors: - name: "Community contribution, refactored into static/dynamic layers" date-released: "2026-07-21" url: "https://skillstore.io/skills/yuan1z0825-nature-figure/audits/5" identifiers: - type: other value: "skillstore:yuan1z0825-nature-figure:audit:5" description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High
73
Architecture
100
Maintainability
87
Content
67
Community
91
Spec Compliance

What You Can Build

Plan a manuscript figure

Turn a research claim into a panel map, evidence hierarchy, export plan, and review checklist.

Revise a submitted figure

Audit typography, statistics, color, source data notes, and journal-ready output formats before resubmission.

Build method comparison panels

Adapt bundled scientific chart patterns into clean comparison figures using real project data.

Try These Prompts

Start a figure plan 
Use nature-figure. I need a Python or R manuscript figure. The main conclusion is [claim]. The data columns are [columns]. Plan the figure before writing code.
Generate backend-specific figure code 
Use nature-figure with Python. Build a multi-panel figure from my dataframe with panels [panel list]. Export editable SVG and a PNG preview.
Audit an existing figure 
Use nature-figure with R. Review my current figure for readability, statistical labeling, color, typography, source-data traceability, and export quality. Return prioritized fixes.
Refactor a demo into a submission figure 
Use nature-figure with Python. Adapt the closest bundled demo pattern to my real data and manuscript claim. Do not reuse demo labels or reveal internal paths.

Best Practices

  • Choose Python or R first and keep the backend exclusive for plotting, previewing, and QA.
  • Define the core conclusion and evidence chain before drawing panels.
  • Export editable SVG or PDF plus a raster preview, and verify source-data traceability.

Avoid

  • Generating mock data when the user has not chosen a backend or supplied a clear target.
  • Mixing Python and R outputs in one figure workflow without user approval.
  • Copying demo labels, private paths, or internal asset names into user-facing deliverables.

Frequently Asked Questions

Does this skill make figures automatically?
It guides planning, code generation, export, and QA. The user still provides data, claims, and review context.
Can I use it with Claude, Codex, and Claude Code?
Yes. The skill metadata lists Claude, Codex, and Claude Code as supported tools.
Why does it ask Python or R first?
The workflow keeps one plotting backend active, so code, preview, export, and QA stay consistent.
Does it only support Nature journals?
No. It targets Nature-style and high-impact manuscript figures, but the same checks help many journals.
Can it revise an existing figure?
Yes. It can audit layout, typography, color, statistics, source-data notes, and export formats.
Does it create dashboards?
No. It is focused on static manuscript figures, not dashboards or product analytics interfaces.

Developer Details

License

MIT

Author version

v2.0.0

Skillstore revision

r1

Repository

https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-figure

Ref

64ca8af0f54a325752f08bd54e52151061ea659a

Maintenance freshness

7/21/2026

Usage

10 downloads · 0 views

File structure

📁 assets/

📁 chart-atlas/

📄 atlas-01-bar-charts.png

📄 atlas-02-line-trends.png

📄 atlas-03-heatmaps.png

📄 atlas-04-scatter-bubble.png

📄 atlas-05-radar-polar.png

📄 atlas-06-distributions.png

📄 atlas-07-forest-interval.png

📄 atlas-08-area-stacked.png

📄 atlas-09-image-plates.png

📄 atlas-10-network-matrix.png

📁 figures4papers/

📁 assets/

📄 Dispersion_motivation.png

📄 Dispersion_observation_distillation.png

📄 Dispersion_observation.png

📄 ImmunoStruct_contrastive.png

📄 ImmunoStruct_results_CEDAR.png

📄 ImmunoStruct_results_IEDB.png

📄 ImmunoStruct_schematic.png

📄 RNAGenScape_schematic.png

📁 figure_brainteaser/

📁 figures/

📄 plot_brute_force.py

📄 plot_correctness_by_category.py

📄 plot_correctness_by_subcategory.py

📄 plot_rewriting.py

📄 plot_selfcorrection_math.py

📁 figure_CellSpliceNet/

📁 figures/

📄 plot_ablation.py

📄 plot_comparison.py

📁 figure_Cflows/

📁 figures/

📄 diffusion_swiss_roll.py

📄 plot_comparison_Ablation.py

📄 plot_comparison_GeneRegulatory.py

📄 plot_comparison_Trajectory.py

📁 figure_Dispersion/

📁 figures/

📄 plot_idea.py

📄 plot_illustration.py

📁 figure_FPGM/

📁 figures/

📄 plot_freq_prior.py

📁 figure_ImmunoStruct/

📁 figures/

📄 plot_bars.py

📄 raw_data.py

📁 figure_ophthal_review/

📁 figures/

📄 plot_composition.py

📄 plot_trend.py

📁 figure_RNAGenScape/

📁 figures/

📄 plot_comparison.py

📄 plot_hole_manifold.py

📄 plot_manifold.py

📄 plot_sweep.py

📁 figure_VIGIL/

📁 figures/

📄 plot_comparison_radar.py

📄 plot_posttraining.py

📁 gallery/

📄 fig1-material-mechanism-rich.png

📄 fig2-spatial-imaging-rich.png

📄 fig3-in-vivo-efficacy-rich.png

📄 fig4-single-cell-systems-rich.png

📄 fig5-validation-perturbation-rich.png

📁 evals/

📄 evals.json

📁 references/

📄 api.md

📄 backend-selection.md

📄 chart-types.md

📄 common-patterns.md

📄 demos.md

📄 design-theory.md

📄 figure-contract.md

📄 figure-legend-conventions.md

📄 nature-2026-observations.md

📄 qa-contract.md

📄 r-template-index.md

📄 r-workflow.md

📄 tutorials.md

📁 static/

📁 core/

📄 contract.md

📄 stance.md

📁 fragments/

📁 backend/

📄 python.md

📄 r.md

📄 manifest.yaml

📄 README.md

📄 SKILL.md