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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.

60

Quality

70%

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tessl review fix ./scientific_writer/.claude/skills/scientific-schematics/SKILL.md

The canonical home for this skill is scientific-schematics in K-Dense-AI/scientific-agent-skills

SKILL.md
Quality
Evals
Security

Quality

Content

73%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 highly actionable with an exemplary validation-and-feedback loop and honest failure handling, and it points to real, well-organized reference files one level deep. Its main weakness is token efficiency: the quick-start command, environment setup, and iteration pitch are each repeated multiple times, and best-practices/checklist content is inlined instead of living in the references.

Suggestions

Collapse the four near-identical usage sections (Quick Start, How to Use This Skill, Command-Line Usage, Getting Started) into a single quick-start section, and merge the three environment-setup blocks into one.

Move the Quick Reference Checklist and the citation policy into a reference file (or fold them into best_practices.md), keeping only a one-line pointer in SKILL.md.

Cut promotional repetition such as "Smart Iteration Benefits", "That's it!", and "No coding, no templates, no manual drawing required" — the mechanics are already stated once in "What happens behind the scenes".

DimensionReasoningScore

Conciseness

The same basic invocation is shown four times ("Quick Start", "How to Use This Skill", "Command-Line Usage", "Getting Started") and environment setup is repeated three times ("Configuration", the Setup subsection of Troubleshooting, and "Environment Setup"), plus promotional padding ("✅ Smart Iteration Benefits", "Works synergistically", "That's it!"). This is 'noticeably verbose; several padded sections' (anchor 2). Not 1 because it avoids explaining basic concepts Claude already knows and the technical sections themselves are tight.

2 / 5

Actionability

Every usage path is copy-paste ready with real flags ("--doc-type journal", "--iterations 2", "-v"), troubleshooting gives exact fixes ("export OPENROUTER_API_KEY='sk-or-v1-...'", "uv pip install requests"), and failure states are described via concrete log fields ("score": null, "reviewed": false, "review_error"). Fully executable guidance covering the common cases — anchor 5.

5 / 5

Workflow Clarity

The generate-review-decide-refine loop is explicitly numbered (1-5 in "What happens behind the scenes") with a validation checkpoint (quality score vs. document-type threshold), a defined feedback loop (improved prompt from critique, regenerate), explicit handling of review failure ("score": null / "reviewed": false), and a post-run verification checklist. Matches the 'clear sequence with explicit validation steps; feedback loops; checklists' anchor.

5 / 5

Progressive Disclosure

Both referenced files exist and hold what the body promises; references are one level deep and clearly signaled both inline ([references/iterative_refinement.md](references/iterative_refinement.md)) and in the "Detailed References" section — good structure per anchor 4. Not 5 because the ~370-line body still inlines substantial material that duplicates the references (the Best Practices Summary overlaps best_practices.md, the iteration walkthrough duplicates iterative_refinement.md, and the long checklists and citation policy belong in a reference).

4 / 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 concrete and domain-specific with good natural trigger vocabulary, and it names its niche clearly. Its main weakness is the missing explicit 'Use when...' trigger guidance, which caps completeness and slightly weakens distinctiveness against generic visualization skills.

Suggestions

Add an explicit trigger clause, e.g. "Use when creating diagrams, schematics, or figures for papers, posters, or grant proposals, or when the user mentions CONSORT/PRISMA flowcharts, network architectures, or signaling pathways."

Include common synonyms such as "figure", "schematic", and "manuscript" to broaden natural trigger coverage.

Trim implementation details (reviewer model names, iteration mechanics) in favor of one more user-facing capability, such as the document-type quality thresholds, to strengthen the 'what' without adding length.

DimensionReasoningScore

Specificity

"Create publication-quality scientific diagrams", "Uses Gemini 3.6 Flash for quality review", and "Only regenerates if quality is below threshold" are concrete actions, matching the 'lists several specific actions; minor gaps' anchor. Not 5 because the actions reduce to one create-action plus review mechanics, with no mention of options like document types or output formats; not 3 because coverage goes beyond 1-2 actions.

4 / 5

Completeness

The 'what' is clear (create publication-quality scientific diagrams with AI generation and threshold-based refinement), but there is no 'Use when...' clause or equivalent explicit trigger guidance — the specialization list only weakly implies 'when'. Per the judging guidelines, a missing 'Use when' clause caps completeness at 3; it is above anchor 2 because the 'what' is specific and detailed.

3 / 5

Trigger Term Quality

Natural terms users would say are present: "flowcharts", "biological pathways", "neural network architectures", "system diagrams", "scientific visualizations". A few common terms are missing ("figure(s)", "schematic", "paper/manuscript"), so it fits anchor 4 rather than the comprehensive synonym coverage of anchor 5; well above the sparse coverage of anchor 3.

4 / 5

Distinctiveness Conflict Risk

The publication-focused scientific-diagram niche ("neural network architectures", "biological pathways", "publication-quality") is mostly distinct, with only minor overlap risk against generic diagramming or data-visualization skills — matching anchor 4. Not 5: without explicit trigger guidance, generic terms like "system diagrams" and "flowcharts" could still match a general diagram skill.

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/claude-scientific-writer
Reviewed

Table of Contents

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