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oral-health-analyzer

分析口腔健康数据、识别口腔问题模式、评估口腔健康状况、提供个性化口腔健康建议。支持与营养、慢性病、用药等其他健康数据的关联分析。

Quality

56%

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tessl review fix ./skills/oral-health-analyzer/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

42%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 impressively concrete clinical thresholds, a weighted scoring model, and five fully-worked scenario flows. Its weaknesses are a monolithic one-file layout with no progressive disclosure, and meaningful token padding from non-operational sections (future extensions, research directions, generic QA). It is a solid capability catalog that needs tightening and restructuring to become an efficient, executable skill.

Suggestions

Delete or relocate the '未来扩展', '研究方向', '持续改进', and generic '质量保证' sections — they consume tokens without changing how Claude executes the skill.

Move the detailed risk-level tables, clinical guideline references, and evaluation-tool material (DMFT, CPI, OHIP-14) into a references/ file, keeping SKILL.md as a concise overview with clearly signaled one-level-deep pointers.

Integrate the data-validation and medical-logic checks as explicit steps inside each scenario workflow (e.g. 'step 0: verify record completeness; step 7: cross-check conclusions against guideline thresholds') instead of a standalone quality section.

DimensionReasoningScore

Conciseness

The ~520-line body is noticeably verbose with several padded, non-operational sections: '未来扩展' (AI imaging, voice input roadmap), '研究方向' (machine learning, microbiome research), '持续改进' (优化用户体验, 扩展功能范围), and generic '质量保证' bullets add nothing Claude needs to execute the skill. It avoids level 1 because it never explains basic concepts Claude already knows — the padding is redundant sections, not tutorial prose.

2 / 5

Actionability

Genuinely concrete guidance exists — clinical thresholds like '探诊深度1-3mm', the weighted scoring formula (口腔卫生习惯40%、检查频率20%...), alert cutoffs (牙痛超过3天建议就医), and numbered 6-step scenario flows with defined outputs. But many sections only list topics without procedure ('统计描述(均值、中位数、标准差)', '相关性分析(Pearson/Spearman相关)', '专家审查(如有)'), so key execution details are missing. This fits 'some concrete guidance but incomplete'.

3 / 5

Workflow Clarity

Each of the five scenarios provides a clear numbered analysis sequence (读取记录 → 分析 → 评估 → 生成报告) with defined inputs and outputs, so the sequence is present. However, validation (数据完整性检查, 医学逻辑检查, 与临床指南对照) lives in a separate '质量保证' section rather than being wired into the workflows as explicit checkpoints — matching 'steps listed but validation gaps; checkpoints missing or implicit'.

3 / 5

Progressive Disclosure

There is no bundle at all (references/, scripts/, assets/ are absent), and all ~520 lines live in SKILL.md with clear section headers throughout. That internal structure plus the amount of detailed reference material (clinical guideline lists, risk-factor tables, evaluation tool names like DMFT/CPI/OHIP-14) that would serve better in separate files matches 'some structure but could be better organized; content that should be separate is inline'. It is not a 2 because headers and nesting make the document navigable.

3 / 5

Total

11

/

20

Passed

Description

55%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 gives a clear, domain-specific picture of what the skill does, but reads like a capability summary rather than a trigger: it lacks any 'when to use' guidance and omits the concrete symptom keywords (cavities, gums, toothache) users would naturally mention. Its self-declared overlap with nutrition/chronic-disease/medication modules also creates trigger-conflict risk.

Suggestions

Add an explicit trigger clause, e.g. '当用户提到口腔健康、龋齿/蛀牙、牙龈出血、牙痛、牙周问题或需要口腔健康分析时使用本技能'.

Include concrete natural-language terms users would say — 龋齿、牙龈出血、牙痛、牙周、口臭 — rather than only the abstract term 口腔健康.

Clarify the boundary with sibling modules: state that nutrition/chronic-disease/medication data is only used as supporting input for oral-health analysis, not analyzed on its own.

DimensionReasoningScore

Specificity

The description lists several distinct domain-specific actions — "分析口腔健康数据", "识别口腔问题模式", "评估口腔健康状况", "提供个性化口腔健康建议", "支持与营养、慢性病、用药等其他健康数据的关联分析" — which matches the anchor 'lists several specific actions; minor gaps in coverage'. It falls short of a 5 because the verbs are generic (analyze/identify/assess/provide) with no concrete deliverables or scope boundaries, and above a 3 because it enumerates multiple distinct capabilities rather than 1-2.

4 / 5

Completeness

The 'what' is clearly answered (four analysis capabilities plus cross-module analysis), but there is no 'Use when...' clause or any equivalent trigger guidance anywhere in the description. Per the rubric guideline, a missing 'Use when' clause caps completeness at 3 ('has a clear what but when is missing or only weakly implied').

3 / 5

Trigger Term Quality

Relevant keywords like 口腔健康, 口腔问题, 营养, 慢性病, 用药 are present, but the natural phrases users would actually say for oral problems — 龋齿/蛀牙 (cavities), 牙龈出血 (bleeding gums), 牙痛 (toothache), 牙周 (periodontal) — are entirely absent. This matches 'some relevant keywords but missing common variations or synonyms'; it is above level 2 because the included terms are domain-relevant rather than generic filler.

3 / 5

Distinctiveness Conflict Risk

The oral-health niche itself is distinct, but the description explicitly claims overlap with sibling domains — "支持与营养、慢性病、用药等其他健康数据的关联分析" — which invites triggering for nutrition, chronic-disease, and medication requests that belong to those modules. This fits 'somewhat specific but could still overlap with similar skills'; it is not a 4 because the cross-module claim materially raises collision risk.

3 / 5

Total

13

/

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.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (528 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

Path

skills/oral-health-analyzer/SKILL.md

Reviewed

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