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scientific-visualization

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

Quality

89%

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SKILL.md
Quality
Evals
Security

Quality

Content

85%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.

An exemplary operational skill: fully executable commands and code, a well-sequenced workflow with validation checkpoints and guardrails, and clean one-level-deep progressive disclosure verified against real bundle files. The main cost is token weight — the honest-encoding and accessibility sections restate standard dataviz knowledge Claude already has, and version-specific gotchas could partially move to references.

Suggestions

Compress the section 2 'Choose an honest encoding' checklist and section 3 accessibility items to one-line guardrails, dropping explanations of principles Claude already knows (zero baselines for bars, area vs radius scaling, WCAG ratio values) — this is the largest token saving (conciseness).

Move the per-library version gotchas (Seaborn missing-row bridging, Plotly/Kaleido format and engine details, TIFF alpha behavior) into references/matplotlib_examples.md or a dated compatibility reference, keeping only the pinned snapshot and pointers in SKILL.md (conciseness, progressive_disclosure).

DimensionReasoningScore

Conciseness

Most of the body is dense, non-obvious, high-value guidance (pinned versions, API migration facts like 'Kaleido 1.4.0 requires Chrome' and 'Plotly 7 removes engine=', exporter semantics), but sections 2–3 restate textbook knowledge Claude already has — 'Bars/areas: normally include zero because length/area is measured from a baseline', 'scale area/volume, not radius/diameter', 'Prefer position on a common scale', and WCAG contrast ratios. That is more than the 'minor instances' of the level-4 anchor, yet there is no padded prose or filler that would justify level 2.

3 / 5

Actionability

Fully executable throughout: copy-paste-ready 'uv run --isolated' commands with complete flags for every bundled CLI, runnable style-context/TwoSlopeNorm/seaborn/export_figure snippets, and coverage of the common cases (styles, color normalization, repeated measures, missing-data gaps, static/interactive export, metadata inspection). The one deferred detail — the import-path bootstrap — is explicitly pointed to in references/matplotlib_examples.md, which matches the level-5 anchor.

5 / 5

Workflow Clarity

Six clearly sequenced stages (define evidence → honest encoding → accessibility → implement → export → inspect/review) with an explicit fallback ('If requirements are not known, create a provisional general figure and label all publisher choices as pending verification'), a dedicated validation stage (inspect metadata, audit contrast, compare dated publisher snapshot, re-check the live journal page immediately before upload), safety guardrails (exporter refuses implicit overwrite), and a final review checklist — matching the level-5 anchor with validation steps, feedback loops, and checklists.

5 / 5

Progressive Disclosure

All five references, seven scripts, and five assets named in the body exist in the bundle. References are one level deep, signaled at point of need, and loaded conditionally ('Read references/journal_requirements.md only after the target and phase are known'), with cataloged Assets and References sections giving one-line descriptions. The SKILL.md works as a clear overview with detail appropriately split out, matching the level-5 anchor.

5 / 5

Total

18

/

20

Passed

Description

88%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 description: third-person voice, explicit and comprehensive 'what', and an explicit 'Use it for...' trigger clause. Its only weaknesses are missing common synonyms (plot/chart/graph/data visualization) that would improve natural trigger matching and sharpen distinctiveness against general visualization skills.

Suggestions

Add natural synonyms users would say — e.g. 'plot', 'chart', 'graph', or 'data visualization' — to the trigger clause to raise trigger-term coverage (trigger_term_quality).

Tie the color/contrast and figure-design triggers more specifically to scientific/publication contexts (e.g. 'publication figures' instead of generic 'figure design') to reduce overlap with general charting skills (distinctiveness_conflict_risk).

DimensionReasoningScore

Specificity

The description lists multiple specific concrete actions — "Creates and audits truthful, accessible, publication-ready scientific figures", "multi-panel layouts, uncertainty and missing-data displays, color/contrast review, image metadata validation, and journal export planning" — with named tools (Matplotlib, Seaborn, Plotly). This matches the anchor for comprehensive coverage of specific actions; it does not fit the level-4 anchor because there are no meaningful gaps in the action list.

5 / 5

Completeness

Both questions are answered explicitly: the "what" is the third-person capability statement ("Creates and audits... scientific figures with Matplotlib, Seaborn, or Plotly") and the "when" is the explicit trigger clause "Use it for figure design, multi-panel layouts, ... journal export planning" with concrete trigger phrases. This matches the level-5 anchor exactly; it is not level 4 because the 'when' is explicit and enumerated rather than merely present.

5 / 5

Trigger Term Quality

Good natural keyword coverage: "figure design", "multi-panel layouts", "uncertainty", "missing-data", "color/contrast review", "journal export", plus all three library names users would mention. It falls short of the level-5 anchor because common synonyms and phrasings users naturally say — "plot", "chart", "graph", "data visualization", "publication figure" — are absent, though coverage is well above the 'some relevant keywords' level-3 anchor.

4 / 5

Distinctiveness Conflict Risk

The scientific-publication framing ("publication-ready", "journal export planning", "image metadata validation") creates a clear niche, but "figure design" and "color/contrast review" are generic enough to overlap with general charting/dataviz skills, matching the 'mostly distinct; minor overlap risk' level-4 anchor rather than the minimal-conflict level-5 anchor.

4 / 5

Total

18

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/scientific-visualization/SKILL.md

Reviewed

Table of Contents

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