data-artist

Create beautiful data visualizations with mathematical elegance, color theory, and narrative design - the "Data is Beautiful" aesthetic.

Quality

61%

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Securityby

Passed

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tessl review fix ./bundled/skills/data-artist/SKILL.md

Quality

Content

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

A well-structured, mostly lean instruction-style skill with concrete tools, palettes, and a clear execution sequence. Main gaps are the absence of executable code examples and lack of external reference files for the heavier reference material.

Suggestions

Add at least one small runnable code example (e.g., a D3 or Chart.js snippet) so the skill offers copy-paste-ready starting points rather than only design guidance.

Move the heavier reference material (full palette library, chart-type matrix, anti-pattern catalog) into one-level-deep reference files (e.g., PALETTES.md, CHART_TYPES.md) with clear links from SKILL.md to improve progressive disclosure.

Tighten or relocate the decorative philosophy/inspiration sections to trim tokens that don't directly guide execution.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean, structured bullet lists that assume Claude's competence without over-explaining basics; minor decorative padding (philosophy principles, 'What Makes Data Beautiful', the boxed output template) could be trimmed. Above 3 because it is efficient overall, below 5 due to the aesthetic-narrative flourishes that don't all earn their tokens.	4 / 5
Actionability	Provides concrete specifics: named tools (D3.js/Chart.js/SVG/Canvas), real hex-code palettes, and a chart-type selection table with use/avoid guidance. Below 5 because there is no copy-paste runnable code and the output section is a placeholder template; above 3 because guidance is concrete and specific rather than pseudocode.	4 / 5
Workflow Clarity	The 'Execution Strategy' gives a clear parallel-then-sequential sequence (story/math/color/data in parallel, then viz), which is easy to follow. Below 5 because there are no validation/checkpoint or error-recovery steps; above 3 because the sequence is explicit and coherent. The destructive/batch cap does not apply to this creative-design skill.	4 / 5
Progressive Disclosure	Single, well-organized SKILL.md with clear section headers and no nested references, making it easy to navigate. Below 5 because substantial reference-style material (palette specs, chart-type table, anti-patterns, inspiration list) is inlined with no one-level-deep external references; above 3 because structure is good and nothing is buried or monolithic.	4 / 5
	Total	16 / 20 Passed

Description

48%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.

The description evokes a compelling aesthetic but is light on concrete actions and entirely missing 'when to use' trigger guidance. Specificity and completeness are the main weaknesses.

Suggestions

Add an explicit 'Use when...' clause naming concrete trigger phrases users would say (e.g., 'Use when creating charts, plots, dashboards, or data stories that should feel beautiful and editorial').

Replace the generic 'Create' with 2-3 concrete actions (e.g., 'Designs perceptually-accurate encodings, builds color palettes, and structures narrative arcs for charts and dashboards').

Include natural synonyms and file/context terms users actually say ('charts', 'graphs', 'plots', 'data visualization', 'dashboard') to improve trigger coverage.

Dimension	Reasoning	Score
Specificity	Names the domain ('data visualizations') with several facets (mathematical elegance, color theory, narrative design) but the only action is the generic 'Create', with no concrete operations listed. It sits at the 'names domain, minimal/generic actions' anchor, below 3 which requires 1-2 concrete actions and above 1 which requires pure abstraction.	2 / 5
Completeness	Gives a clear 'what' (create data visualizations with a specific aesthetic) but has no 'when'/'Use when' trigger guidance, which the rubric caps at 3. It is above 2 because the 'what' is clear, and not 4 because no explicit trigger conditions exist.	3 / 5
Trigger Term Quality	Contains moderately natural terms like 'data visualizations' and 'color theory' but misses common synonyms users say ('charts', 'graphs', 'plots'). Matches the 'some relevant keywords but missing common variations/synonyms' anchor, below the good-coverage 4 and above the single-generic-keyword 2.	3 / 5
Distinctiveness Conflict Risk	The 'Data is Beautiful' aesthetic combined with color theory and narrative design carves a fairly distinct niche with only minor overlap risk against generic charting skills. Below 5 because the trigger phrasing is somewhat broad, above 3 because it is clearly more specific than generic document/data skills.	4 / 5
	Total	12 / 20 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/data-artist/SKILL.md
Commit: d5ae560

Reviewed: 1 day ago

Table of Contents

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