sn-infographic

Generates professional infographics with various layout types and visual styles. Analyzes content, recommends layout and style, and generates publication-ready infographics. Use when user asks to create "infographic", "信息图", "visual summary", or "可视化".

Quality

82%

Does it follow best practices?

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Quality

Content

81%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 exceptionally actionable and well-sequenced workflow with strong validation checkpoints and a verified one-level-deep reference structure. The main weakness is conciseness: several sections over-explain intent and repeat envelope-parsing caveats that a competent agent does not need.

Suggestions

Trim the meta-commentary in Step 1 (e.g. 'Do not over-read the step name', the subagent-decomposition disambiguation) down to one line; it assumes Claude will misread the step and pads the context unnecessarily.

Consolidate the repeated JSON-envelope caveat ('status != ok has no .result -> null slips past jq') into a single note near the JSON-parsing section instead of restating it before each Step 2.0/2.3 block.

Consider moving the Input Specification and API Configuration tables into a reference file (e.g. references/runtime-parameters.md already covers some) so the SKILL.md body stays a lean overview.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with executable snippets, but padded with extensive conceptual re-explanation ('Do not over-read the step name', repeated JSON-envelope caveats, anti-drift style paragraphs) that assumes Claude needs hand-holding Claude does not need; noticeably above a 2 but not lean enough for a 4.	3 / 5
Actionability	Provides fully executable, copy-paste bash snippets for every pipeline step plus concrete parameter tables, JSON schemas, and exact command flags covering the common cases.	5 / 5
Workflow Clarity	Steps 0-4 are explicitly numbered and sequenced with validation checkpoints at every external call (status==ok guards, extract_json.py exit checks, Error Flow on failure, early-termination gate, conservative fallbacks), satisfying the destructive/batch feedback-loop requirement.	5 / 5
Progressive Disclosure	Body is a clear overview with well-signaled one-level-deep references into references/ (verified to exist: 87 layouts, 66 styles, and all named .md files); just short of 5 because the inline Input/API/workflow tables are heavy enough that some could live in reference files.	4 / 5
	Total	17 / 20 Passed

Description

83%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 third-person description that clearly states capabilities and provides an explicit 'Use when' trigger clause with bilingual keywords. It is comprehensive enough for accurate skill selection, with only minor coverage gaps in trigger synonyms.

Dimension	Reasoning	Score
Specificity	Names the domain and lists three concrete actions ('Analyzes content, recommends layout and style, and generates publication-ready infographics'); slightly below 5 because it does not enumerate the breadth of layout/style types or the multi-round review feature.	4 / 5
Completeness	Explicitly answers both what ('Generates...infographics...Analyzes content, recommends layout and style') and when ('Use when user asks to create...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Includes strong natural terms ('infographic', 'visual summary', '信息图', '可视化') with both English and Chinese synonyms; just short of 5 because it omits common single-word variants like 'infographics' or a bare 'diagram' trigger in the description itself.	4 / 5
Distinctiveness Conflict Risk	The infographic niche and bilingual trigger phrases are fairly distinct, but 'visual summary' / 'diagram' overlap with general data-visualization skills, so not a clean 5.	4 / 5
	Total	17 / 20 Passed

Validation

68%

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 — 11 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (555 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning
metadata_field	'metadata' should map string keys to string values	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning
referenced_paths_exist	Referenced path issues: 1 missing	Warning

	Total	11 / 16 Passed

Repository: OpenSenseNova/SenseNova-Skills
Path: skills/sn-infographic/SKILL.md
Commit: 61b60bb

Reviewed: 8 days ago

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