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datavis

Comprehensive data visualization toolkit for creating beautiful, mathematically elegant visualizations with D3.js, Chart.js, and custom SVG. Use when (1) building interactive data visualizations, (2) designing color palettes for charts, (3) choosing scales and visual encodings, (4) creating data pipelines from Census/SEC/Wikipedia APIs, (5) crafting narrative-driven data stories, (6) making perceptually accurate charts, or (7) implementing force-directed networks, timelines, or geographic maps.

76

Quality

93%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

87%

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

A tight, highly actionable reference with executable code and real bundled scripts, organized for easy navigation. The main gap is the data-pipeline workflow, which names a validation step but does not spell out the failure-and-retry feedback loop expected for batch operations.

Suggestions

Make the data pipeline's feedback loop explicit: after 03_validate.py, add 'If validation fails: fix the issue in 02_clean_data.py and re-run from validation — only proceed to 04_export.py when checks pass.'

Add a short runbook-style sequence (with the actual script commands) for the pipeline rather than only the file-tree template, so the steps are executable in order.

Tie the Quality Checklist items back to specific pipeline stages or script outputs so each checkpoint has a concrete verification command.

DimensionReasoningScore

Conciseness

The body is lean reference material — a scale-selection table, compact color arrays, and tight D3 snippets — and does not re-explain concepts Claude already knows; the brief 'Philosophy' framing is the only flavor text and it stays short.

3 / 3

Actionability

Provides fully executable code (d3.forceSimulation, scaleSqrt, responsive viewBox) and copy-paste-ready CLI invocations for the bundled scripts (e.g. 'scripts/color-palette.py --type sequential --hue blue --steps 9').

3 / 3

Workflow Clarity

The data pipeline lists sequenced steps (01_fetch → 02_clean → 03_validate → 04_export) and a Quality Checklist, but lacks an explicit validate→fix→retry feedback loop for these batch API operations, so checkpoints are implicit rather than spelled out.

2 / 3

Progressive Disclosure

A single well-sectioned SKILL.md points one level deep to real bundled scripts (color-palette.py, analyze-distribution.py, d3-scaffold.py, all present in scripts/) with clear navigation; no nested or missing references.

3 / 3

Total

11

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12

Passed

Description

100%

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 names concrete capabilities and provides an explicit, enumerated 'Use when' trigger list covering seven distinct scenarios. It is specific, complete, and unlikely to conflict with sibling skills.

DimensionReasoningScore

Specificity

Lists many concrete actions — 'creating...visualizations with D3.js, Chart.js, and custom SVG', 'designing color palettes', 'choosing scales and visual encodings', 'creating data pipelines from Census/SEC/Wikipedia APIs', 'implementing force-directed networks, timelines, or geographic maps' — matching the multiple-specific-actions anchor.

3 / 3

Completeness

Explicitly answers both what ('Comprehensive data visualization toolkit for creating...visualizations with D3.js, Chart.js, and custom SVG') and when ('Use when (1)...(7)...'), with explicit numbered triggers.

3 / 3

Trigger Term Quality

Covers natural terms users would say — 'data visualizations', 'color palettes for charts', 'D3.js', 'Chart.js', 'timelines', 'geographic maps', 'data pipelines' — with good breadth of common variations.

3 / 3

Distinctiveness Conflict Risk

A clear niche anchored to specific tools (D3.js, Chart.js) and specific data sources (Census/SEC/Wikipedia APIs) with distinct triggers, making overlap with other skills unlikely.

3 / 3

Total

12

/

12

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
foryourhealth111-pixel/Vibe-Skills
Reviewed

Table of Contents

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