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scientific-visualization

Create and audit truthful, accessible, publication-ready scientific figures with Matplotlib, Seaborn, or Plotly. Use for figure design, multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning.

75

Quality

92%

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SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A high-quality, action-oriented SKILL.md body: executable code, verified bundled tooling, a sequenced workflow with validation, and clean progressive disclosure to real reference files. Only minor conciseness trimming is warranted.

DimensionReasoningScore

Conciseness

The body is dense and assumes Claude's competence — it does not explain what Matplotlib or a PDF is, and dated version pins are correctly isolated in a 'Pinned snapshot' section — but the long honest-encoding bullet list and the citation block could be trimmed slightly, keeping it just below the leanest anchor.

4 / 5

Actionability

Provides copy-paste-ready, executable code (OO Matplotlib, TwoSlopeNorm color normalization, Seaborn errorbar API, export_figure call) and complete CLI invocations with flags; the referenced symbols (style_context, export_figure) match the bundled scripts' signatures.

5 / 5

Workflow Clarity

A clearly sequenced six-step workflow with an inspection/review step, a final review checklist acting as a validation feedback loop, and an exporter that refuses implicit overwrite as an explicit checkpoint.

5 / 5

Progressive Disclosure

Well-organized overview with clearly signaled, one-level-deep references to verified files in references/, scripts/, and assets/; content is appropriately split across the bundle with easy navigation.

5 / 5

Total

19

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, third-person description that pairs concrete capabilities with an explicit 'Use for...' trigger clause and a well-scoped niche. Keyword coverage is broad but could add user synonyms (plots/charts) and common image file extensions.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Create and audit', 'figure design', 'multi-panel layouts', 'uncertainty and missing-data displays', 'color/contrast review', 'image metadata validation', 'journal export planning' — alongside the three named libraries, giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Explicitly answers both 'what' ('Create and audit truthful, accessible, publication-ready scientific figures...') and 'when' ('Use for figure design, multi-panel layouts, ... journal export planning') with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good coverage of natural terms ('scientific figures', 'multi-panel layouts', 'color/contrast review') plus tool names (Matplotlib, Seaborn, Plotly), but missing common synonyms such as 'plots'/'charts' and file extensions like .pdf/.png that users might say.

4 / 5

Distinctiveness Conflict Risk

A clear niche — scientific figures with Matplotlib/Seaborn/Plotly and journal-export planning — with distinct triggers and minimal overlap risk against general plotting or document skills.

5 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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