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scientific-visualization

Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.

67

Quality

80%

Does it follow best practices?

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tessl review fix ./researchclaw/skills/builtin/experiment/scientific-visualization/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

80%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 tight, reference-style skill that delivers publication-figure specs concisely with strong organization. Its main gap is the absence of an explicit sequenced workflow with validation checkpoints for producing and exporting a figure.

Suggestions

Add a short numbered workflow (design → size → color-check → annotate → export → validate) with an explicit validation checkpoint before submission.

Include one complete copy-paste matplotlib/seaborn example showing the recommended sizing, colorblind palette, and export-to-vector pattern.

Add a final verification step in the export checklist (e.g., open the exported file at print size and confirm 6pt readability) to close the feedback loop.

DimensionReasoningScore

Conciseness

Lean bullet lists with no padding or explanations of concepts Claude already knows; every line delivers an actionable specification.

5 / 5

Actionability

Concrete, specific guidance throughout (sizing in inches/mm, named palettes, DPI values, panel-label conventions) with one executable code snippet, but most guidance is directive rather than copy-paste code.

4 / 5

Workflow Clarity

Content is organized as topical checklists rather than a sequenced workflow; the final 'Export and Quality Checklist' provides verification items but there are no validation checkpoints or feedback loops for the multi-step figure-production process.

3 / 5

Progressive Disclosure

A single well-organized overview under 50 lines with no external references needed; section headers make navigation easy and no content is inappropriately inlined.

5 / 5

Total

17

/

20

Passed

Description

80%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 well-crafted description that states the domain, concrete capabilities, and a clear 'Use when' trigger in third person. It distinguishes itself clearly and avoids vague fluff.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions ('creating journal submission figures with proper formatting, accessibility, and statistical annotations') with only minor coverage gaps around export formats.

4 / 5

Completeness

Both the 'what' (publication-ready figure design) and the 'when' ('Use when creating journal submission figures...') are present, though the trigger guidance could be slightly more explicit with multiple trigger scenarios.

4 / 5

Trigger Term Quality

Includes strong natural terms ('journal submission figures', 'matplotlib', 'seaborn', 'publication') that users would say, but misses common synonyms like 'plots' or file extensions.

4 / 5

Distinctiveness Conflict Risk

The publication-figure niche with matplotlib/seaborn is a clear, distinct trigger unlikely to collide with other skills.

5 / 5

Total

17

/

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
aiming-lab/AutoResearchClaw
Reviewed

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