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scientific-schematics

Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

59

Quality

68%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./skills/scientific-schematics/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

71%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 body is well-structured with strong progressive disclosure and actionable, executable commands, but it is noticeably repetitive — the same basic usage and caveats appear in four sections — which hurts token efficiency.

Suggestions

Collapse Quick Start, How to Use, Command-Line Usage, and Getting Started into a single usage section; keep one canonical command example and remove the repeated 'no coding, no templates' line.

Trim the 'Smart Iteration Benefits' checkmark list and the duplicated PNG-only caveat (stated in both Overview and 'What the pipeline cannot do') to a single location.

DimensionReasoningScore

Conciseness

Mostly avoids explaining concepts Claude already knows, but the same one-line command and 'no coding/templates/manual drawing' message recur across Quick Start, How to Use, Command-Line Usage, and Getting Started, and the 'Smart Iteration Benefits' checklist reads as padding; could be tightened noticeably.

3 / 5

Actionability

Provides concrete, copy-paste-ready commands with real worked examples across document types and references an existing script, with only minor gaps where guidance is prompt-engineering advice rather than executable steps.

4 / 5

Workflow Clarity

The generate-review-refine loop is clearly sequenced with an explicit decision checkpoint and feedback loop, plus a review-log verification checklist; minor validation gaps (e.g. checkpoint placement is scattered) keep it just below a 5.

4 / 5

Progressive Disclosure

SKILL.md is a clear overview with well-signaled, one-level-deep references to real files (iterative_refinement.md and best_practices.md, both present in references/) and bulk detail appropriately pushed into those files, making navigation easy.

5 / 5

Total

16

/

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 distinctive with strong trigger terms, but it lacks an explicit 'Use when...' trigger clause, leaving the 'when to use' guidance only implied and capping completeness.

Suggestions

Add an explicit 'Use when...' clause naming concrete user triggers (e.g. 'Use when creating figures for papers, posters, or talks: neural network architectures, flowcharts, pathways, system diagrams').

Consider adding the output format hint (PNG) and common user phrasings like 'scientific figure' or 'paper diagram' to round out trigger-term coverage.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions ('Create publication-quality scientific diagrams', 'Uses Gemini 3.6 Flash for quality review', 'Only regenerates if quality is below threshold') plus a list of specialization areas, but stops short of comprehensive multi-action coverage.

4 / 5

Completeness

Has a clear 'what' but no explicit 'Use when...' trigger clause — the 'when' is only weakly implied via 'Specialized in...', which per the guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

Good natural keyword coverage with synonyms across diagram types ('neural network architectures', 'system diagrams', 'flowcharts', 'biological pathways', 'scientific visualizations'), though a few common user phrasings or file extensions are absent.

4 / 5

Distinctiveness Conflict Risk

Clear niche (publication-quality scientific schematics) with distinct, domain-specific triggers and only minor overlap risk with general diagram or visualization skills.

4 / 5

Total

15

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

Total

15

/

16

Passed

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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