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figure-legend-gen

Generate standardized figure legends for scientific figures and charts; use when preparing publication-ready legends that summarize design, variables, sample size, and key statistical notes.

59

Quality

68%

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./scientific-skills/Academic Writing/figure-legend-gen/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

53%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 has solid actionable command/parameter guidance and real bundled references, but is weighed down by extensive generic process boilerplate that inflates token cost without adding figure-legend-specific knowledge. Tightening the workflow into concrete validation checkpoints and removing redundant gating sections would materially improve it.

Suggestions

Remove or consolidate the generic boilerplate sections (Output Requirements, Response Template, Input Validation, Failure Handling, User Checkpoints, Output Contract, Security Checklist, Risk Assessment, Evaluation Criteria, Lifecycle Status) that restate knowledge Claude already has.

Rewrite the Workflow as a concrete, sequenced recipe with an explicit validate->fix->retry loop for legend generation, instead of abstract gating prose.

Fix the malformed example ('python scripts/main.py --image.png --type scatter --language zh') to use '--input image.png', and either add the missing requirements.txt or drop the 'pip install -r requirements.txt' reference.

DimensionReasoningScore

Conciseness

The body is noticeably verbose, padding SKILL.md with generic boilerplate sections (Output Requirements, Response Template, Input Validation, Failure Handling, User Checkpoints, Output Contract, Security Checklist, Risk Assessment, Evaluation Criteria, Lifecycle Status) that restate process knowledge Claude already has rather than figure-legend specifics.

2 / 5

Actionability

Provides concrete executable usage ('python scripts/main.py --input <image_path> --type <chart_type>'), a real parameter table, and working examples tied to the bundled script, with only minor gaps such as the malformed '--image.png' example missing '--input'.

4 / 5

Workflow Clarity

A sequence exists, but the Workflow steps are abstract gating prose rather than a concrete recipe, and the core generation operation lacks an explicit validate->fix->retry loop; the batch/destructive cap (User Checkpoints mention batch processing) holds this at 3.

3 / 5

Progressive Disclosure

References are one level deep and clearly signaled to real bundle files (references/legend_templates.md, references/academic_style_guide.md, scripts/main.py), but substantial generic boilerplate is inlined in SKILL.md rather than split out, and the referenced requirements.txt does not exist.

4 / 5

Total

13

/

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.

The description is strong: it states a concrete capability and an explicit 'use when' trigger with specific publication-related keywords, covering both what and when clearly. It could be nudged toward a perfect score by adding synonyms like 'captions' and sharpening distinctiveness from general academic-writing skills.

Suggestions

Add common synonyms such as 'figure captions' or 'chart captions' to broaden natural trigger coverage.

Sharpen distinctiveness by foregrounding the scientific/publication niche more prominently against general academic writing skills.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions ('Generate standardized figure legends', 'summarize design, variables, sample size, and key statistical notes'), with only minor coverage gaps versus the comprehensive 5 anchor.

4 / 5

Completeness

Explicitly answers both what ('Generate standardized figure legends for scientific figures and charts') and when ('use when preparing publication-ready legends...') with concrete trigger phrases, matching the 5 anchor.

5 / 5

Trigger Term Quality

Includes natural terms users would say ('figure legends', 'scientific figures and charts', 'publication-ready legends') but misses common synonyms such as 'captions', so it is good rather than comprehensive.

4 / 5

Distinctiveness Conflict Risk

Targets a clear niche (scientific figure legends) with specific triggers, but has minor overlap risk with general academic/publication-writing skills, fitting the 'mostly distinct' 4 anchor.

4 / 5

Total

17

/

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
aipoch/medical-research-skills
Reviewed

Table of Contents

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