infographics

Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.

Quality

72%

Does it follow best practices?

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Securityby

Low

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tessl review fix ./skills/infographics/SKILL.md

Quality

Content

85%

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

The body is highly actionable with a clear validated workflow and well-organized one-level-deep references. Its main weakness is verbosity from repeating the full command for every type and restating the iteration flow twice.

Suggestions

Factor the repeated generation command into one base example and show only the differing flags (--type, --style, --palette) per type to cut redundancy.

Remove the ASCII 'How It Works' diagram or the 'Smart Iteration Benefits' emoji list, since both restate the numbered workflow already given.

Consider moving the per-type templates into references/infographic_types.md (already referenced) to slim the main file.

Dimension	Reasoning	Score
Conciseness	Mostly actionable, but the full `generate_infographic.py` invocation is repeated ~15 times across types, and the emoji 'Smart Iteration Benefits' list plus ASCII diagram restate content already covered.	2 / 3
Actionability	Provides copy-paste-ready executable bash commands for all 10 types, a complete CLI reference, and concrete prompt-engineering examples.	3 / 3
Workflow Clarity	The 'behind the scenes' sequence is numbered with an explicit Gemini review validation checkpoint and an improve-and-regenerate feedback loop gated on a quality threshold.	3 / 3
Progressive Disclosure	SKILL.md is an overview with a clearly signaled 'Reference Files' section pointing one level deep to three real references (infographic_types, design_principles, color_palettes) plus the generation script.	3 / 3
	Total	11 / 12 Passed

Description

60%

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 is specific and capability-rich but reads more like a feature list than a trigger. It answers 'what' well but omits an explicit 'Use when…' clause, capping completeness and distinctiveness.

Suggestions

Add an explicit 'Use when…' clause naming concrete user situations (e.g., 'Use when creating timeline visualizations, comparing options, or summarizing statistics as visuals').

Include natural trigger terms users would actually say ('data visualization', 'charts', 'timelines', 'side-by-side comparison') to improve trigger matching.

Sharpen distinctiveness by contrasting with adjacent skills (e.g., 'Use scientific-schematics for technical/circuit diagrams instead').

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — create infographics, smart iterative refinement, Gemini quality review, research-lookup/web search integration — plus concrete counts (10 types, 8 styles).	3 / 3
Completeness	Clearly answers 'what' the skill does, but lacks a 'Use when…' trigger clause, so the 'when' guidance is only implied.	2 / 3
Trigger Term Quality	The natural term 'infographics' is present, but common variations users would say (data visualization, charts, diagrams, timelines) are absent.	2 / 3
Distinctiveness Conflict Risk	'Infographics' is a recognizable niche, but the description could overlap with adjacent skills (generate-image, scientific-schematics, scientific-slides) and offers no distinct trigger terms.	2 / 3
	Total	9 / 12 Passed

Validation

81%

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 — 13 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (566 lines); consider splitting into references/ and linking	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

	Total	13 / 16 Passed

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/infographics/SKILL.md
Commit: 757b63b

Reviewed: about 11 hours ago

Table of Contents

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