data-viz-renderer

Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards. Offers 8 color palettes and built-in icons with no external dependencies. Triggered when users request data visualization, infographics, charts, or dashboards.

Quality

88%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

77%

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

Highly actionable body with executable commands and complete per-type JSON examples, plus a clear single-step workflow. It loses points for promotional 'Design Highlights'/'Use Cases' padding and for keeping all detailed reference material inline rather than splitting it into separate files.

Suggestions

Remove or trim the 'Design Highlights' and 'Use Cases' sections; they promote the tool rather than guide execution and add tokens without earning their place.

Move the per-type data-format examples and output/error format into a reference file (e.g. reference/CONFIG.md) linked one level deep, keeping SKILL.md as a concise overview.

If keeping inline examples, add a one-line validation note for malformed config (the script reports status:'error') so users know to check the JSON result before relying on the output file.

Dimension	Reasoning	Score
Conciseness	Mostly efficient and free of basic-concept explanations, but the 'Design Highlights' and 'Use Cases' sections read as promotional padding that does not earn its tokens for execution.	2 / 3
Actionability	Provides executable commands (python3 scripts/build_infographic.py config.json, stdin variant), a complete config schema table, and copy-paste-ready JSON examples for every type.	3 / 3
Workflow Clarity	Single-action tool with an unambiguous invocation, documented stdout JSON status, output path behavior, and error format — clear enough for a simple skill to score 3.	3 / 3
Progressive Disclosure	Well-sectioned, but ~170 lines of inline data-format and output specs that could live in separate reference files are all kept in SKILL.md rather than split one level deep.	2 / 3
	Total	10 / 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 trigger clause covering natural user terms. It clearly answers both what the skill does and when to invoke it.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions and output types — 'Generate self-contained HTML/SVG infographics', 'stat cards, bar charts, flow diagrams, and mixed dashboards', '8 color palettes and built-in icons'.	3 / 3
Completeness	Explicitly states what it does (generate HTML/SVG infographics from JSON) and when to use it ('Triggered when users request data visualization, infographics, charts, or dashboards').	3 / 3
Trigger Term Quality	Includes natural user-facing terms — 'data visualization, infographics, charts, or dashboards' — covering the common phrasings a user would say.	3 / 3
Distinctiveness Conflict Risk	A clear niche (JSON-driven self-contained HTML/SVG infographics) with distinct triggers and third-person voice, unlikely to collide with other skills.	3 / 3
	Total	12 / 12 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: zebbern/claude-code-guide
Path: skills/data-viz-renderer/SKILL.md
Commit: af7d491

Reviewed: about 9 hours ago

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