infographics

Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.

Quality

73%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

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

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.

The content is highly actionable with a clear, well-validated iterative workflow and good progressive-disclosure structure backed by real reference files. The main weakness is conciseness: redundant command repetitions and inlined reference-style material inflate the token budget.

Suggestions

Collapse the 10 near-identical per-type command blocks into one parameterized example plus a type table, moving extended per-type templates entirely into references/infographic_types.md.

Trim the opening definition ('Infographics are visual representations of information...') and the Overview restating the pipeline, since the Quick Start already covers it.

Move the full industry-style and colorblind-palette tables into the existing reference files, keeping only a brief pointer and one example inline.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient with clear sections, but it repeats the same generate_infographic.py invocation ~15 times across type examples and explains concepts Claude already knows ('Infographics are visual representations of information...'). It is not 4 because the redundant command repetitions and preamble padding could be trimmed without losing clarity.	3 / 5
Actionability	Provides fully executable, copy-paste-ready commands with concrete flags, output paths, and realistic prompts covering all 10 types, plus a complete CLI reference and --list-options fallback. Specific examples cover the common cases end to end.	5 / 5
Workflow Clarity	The generate → Gemini review → score-vs-threshold → iterate loop is clearly sequenced with an explicit decision checkpoint and early-stop feedback loop, including a review log schema and a Quick Reference Checklist. It is not 4 because validation checkpoints and the recovery loop are explicit rather than implicit.	5 / 5
Progressive Disclosure	SKILL.md is structured as an overview with a clearly signaled Reference Files section pointing to three real one-level-deep files (infographic_types.md, design_principles.md, color_palettes.md). It is not 5 because large blocks that belong in references — all 10 type examples and full style/palette tables — are inlined rather than split out.	4 / 5
	Total	17 / 20 Passed

Description

66%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 is specific and well-stocked with concrete capabilities and natural trigger terms, but it omits any explicit 'when to use' guidance. Adding a 'Use when...' clause with concrete triggers would lift completeness and distinctiveness.

Suggestions

Append an explicit 'Use when...' clause, e.g. 'Use when creating infographics, data visualizations for reports or social media, timeline or comparison graphics, or when the user asks for an infographic.'

Add natural synonyms users say — 'data visualization', 'charts', 'visual summary' — to broaden trigger-term coverage.

Briefly disambiguate from adjacent skills (e.g. 'for technical diagrams use scientific-schematics') to reduce conflict risk.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions — 'Create professional infographics', 'smart iterative refinement', 'quality review', 'Integrates research-lookup and web search', and specific counts of types/styles/palettes — with only minor coverage gaps. It is not a 5 because actions like generate, iterate, and research are stated somewhat abstractly rather than as a fully enumerated set.	4 / 5
Completeness	The 'what' is clearly stated (create infographics with iterative refinement and AI quality review), but there is no explicit 'Use when...' clause or equivalent trigger guidance. Per the rubric, a missing 'when' clause caps completeness at 3; it cannot be 4 without explicit usage triggers.	3 / 5
Trigger Term Quality	'infographics', 'infographic types', and 'colorblind-safe palettes' are natural terms users would say, with good coverage of the domain. It stops short of 5 because common synonyms a user might say — 'data visualization', 'charts', 'diagrams', 'visual summaries' — are absent.	4 / 5
Distinctiveness Conflict Risk	'Infographics' is a clear niche and the mention of specific types/styles/palettes distinguishes it from generic image generation. It is not 5 because there is residual overlap risk with sibling skills like generate-image and scientific-schematics, which the description does not explicitly disambiguate.	4 / 5
	Total	15 / 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
skill_md_line_count	SKILL.md is long (564 lines); consider splitting into references/ and linking	Warning

	Total	15 / 16 Passed

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

Reviewed: 1 day 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.