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skin-health-analyzer

Analyze skin health data, identify skin problem patterns, assess skin health status. Supports correlation analysis with nutrition, chronic diseases, and medication data.

44

Quality

46%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/skin-health-analyzer/SKILL.md

The canonical home for this skill is skin-health-analyzer in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

35%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 skill body is a comprehensive but overly verbose feature/medical-reference specification with no executable code, commands, or external reference files. Workflows are sequenced but lack validation checkpoints, and the monolithic structure buries content that should be progressively disclosed.

Suggestions

Move the medical reference material (nutrient effects, drug reactions, disease-skin associations, ingredient lists) into separate files under references/ and keep SKILL.md as a lean overview with one-level-deep links.

Add concrete, executable guidance — data schemas/fields to read, the actual ABCDE scoring formula as an applied rule, and example output formats — instead of describing outputs in the abstract.

Add validation checkpoints to the scenario workflows (e.g. verify data completeness before scoring, flag low-confidence assessments for clinician review) and keep the medical disclaimer as an explicit gate before any risk output.

DimensionReasoningScore

Conciseness

At ~700 lines the body is a dense medical reference dump — vitamin effects, drug reactions, disease-skin associations, ingredient lists — much of which Claude already knows, matching the 2-anchor 'noticeably verbose; several unnecessary explanations or padded sections' rather than the more efficient 3-anchor.

2 / 5

Actionability

The guidance is high-level and descriptive ('读取最近6个月的所有皮肤健康记录', '计算健康评分变化') with no executable code or commands; the only semi-concrete artifact is the ABCDE scoring rubric, fitting the 2-anchor 'minimal concrete guidance; high-level hints but missing the specific steps to execute'.

2 / 5

Workflow Clarity

Usage scenarios provide numbered sequences (e.g. scenario 1 has a 6-step flow), so a rough sequence exists, but there are no validation checkpoints or feedback loops, matching the 3-anchor 'steps listed but validation gaps; checkpoints missing or implicit'.

3 / 5

Progressive Disclosure

The file has clear section headers and is well-organized, but it is a single ~700-line monolith with no bundle files and content (medical reference tables, correlation detail, ABCDE algorithm) that belongs in separate reference files is all inlined, matching the 3-anchor 'some structure but content that should be separate is inline'.

3 / 5

Total

10

/

20

Passed

Description

58%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 states concrete capabilities clearly and carves out a distinct skin-health niche, but it lacks any explicit 'Use when...' trigger guidance and leans on technical phrasing rather than the natural terms users would say. It is competent but not yet optimal as a skill description.

Suggestions

Add an explicit 'Use when...' clause naming concrete user triggers (e.g. 'Use when analyzing skin health records, monitoring moles, or assessing skin cancer/acne risk').

Replace technical phrasing like 'skin problem patterns' with natural user terms and synonyms (acne, moles, skincare, skin cancer, rashes) to improve trigger matching.

Mention the supported data inputs (skin records, mole tracking, skincare logs) so Claude can recognize when the relevant data is present.

DimensionReasoningScore

Specificity

Names the domain and lists several concrete actions — 'Analyze skin health data', 'identify skin problem patterns', 'assess skin health status', 'correlation analysis with nutrition, chronic diseases, and medication data' — with only minor coverage gaps, so it sits above the 3-anchor (1-2 actions) but below the fully comprehensive 5-anchor.

4 / 5

Completeness

The 'what' is clearly stated (analyze, identify patterns, assess status, correlation analysis) but there is no 'Use when...' or equivalent explicit trigger guidance, which per the judging guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

It contains relevant keywords ('skin health', 'skin problem patterns', 'nutrition', 'chronic diseases', 'medication') but leans technical and omits the natural terms users actually say for this domain ('acne', 'moles', 'skincare', 'skin cancer risk'), matching the 'some relevant keywords but missing common variations' anchor rather than the 4-anchor.

3 / 5

Distinctiveness Conflict Risk

The skin-health niche plus explicit cross-module correlation (nutrition, chronic diseases, medication) is mostly distinct from other skills with only minor overlap risk against general health-analysis skills, fitting the 4-anchor better than the 3-anchor.

4 / 5

Total

14

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (711 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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