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

87%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Quality

Content

86%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.

Highly actionable content: executable commands, complete JSON schemas for all four infographic types, and an accurate single-script bundle. Weaknesses are minor — marketing-style Design Highlights/Use Cases sections and no error-recovery guidance after the documented error output.

Suggestions

Trim the 'Design Highlights' and 'Use Cases' sections to a single line each (or drop them) — they describe benefits rather than instruct, and their facts (self-contained output, zero dependencies) already appear elsewhere.

Add one line after the error-output example, e.g. 'On error, fix the listed fields in config.json and re-run', to close the validate -> fix -> retry loop.

Consider adding natural synonyms such as 'graphs' or 'plots' to the frontmatter description's trigger clause to broaden natural-language matching.

Dimension	Reasoning	Score
Conciseness	The body is efficient — a config field table, complete JSON examples per type, and no explanations of concepts Claude already knows — but "Design Highlights" ("Zero external dependencies", "Professional palettes") and "Use Cases" are promotional padding that could be trimmed, matching the anchor for minor over-explanation rather than the every-token-earns-its-place score-5 case.	4 / 5
Actionability	"python3 scripts/build_infographic.py config.json" and the stdin variant are copy-paste ready, and complete executable JSON examples cover all four types (stats, comparison, flow, dashboard) plus the full field table, palette values, icon list, and success/error output formats — matching the fully-executable, common-cases-covered score-5 anchor.	5 / 5
Workflow Clarity	As a simple single-action skill (build a config, run the script) the usage is unambiguous and even the error output shape is documented, but there is no instruction on what to do when status is "error" (no validate -> fix -> retry loop), so it sits at clear-sequence-with-minor-validation-gaps rather than score 5.	4 / 5
Progressive Disclosure	Sections are clearly organized and the single bundle file referenced (scripts/build_infographic.py) exists, is one level deep, and matches the documented palettes and config fields; the inline per-type format examples are the core instructions for a single-script skill and belong in SKILL.md, so structure is appropriately split with no nesting.	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, specific, and complete with an explicit trigger clause naming all four artifact types. The only meaningful gap is that a few natural synonyms (graphs, plots) and format-specific triggers are absent, leaving slight overlap risk with generic visualization skills.

Dimension	Reasoning	Score
Specificity	"Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards" plus "Offers 8 color palettes and built-in icons" names multiple concrete actions covering all four output types with comprehensive coverage, matching the score-5 anchor; nothing material is left to inference.	5 / 5
Completeness	The "what" is explicit (generate self-contained HTML/SVG infographics from JSON, with the four listed types) and the "when" is explicit ("Triggered when users request data visualization, infographics, charts, or dashboards"), matching the anchor that requires both with concrete trigger phrases; it is not score 4 because the when-clause is already specific and concrete.	5 / 5
Trigger Term Quality	"Triggered when users request data visualization, infographics, charts, or dashboards" provides good natural-phrase coverage, but common synonyms like "graphs", "plots", or "figures" are missing, so it falls short of the comprehensive synonym/extension coverage of the score-5 anchor.	4 / 5
Distinctiveness Conflict Risk	The JSON-to-self-contained-HTML/SVG infographic niche with zero external dependencies is mostly distinct, but trigger words like "charts" and "dashboards" overlap with general charting/BI skills, so it is not the minimal-conflict score-5 case.	4 / 5
	Total	18 / 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: zebbern/claude-code-guide
Path: skills/data-viz-renderer/SKILL.md
Commit: a2e6ee3

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.