CtrlK
BlogDocsLog inGet started
Tessl Logo

ai-analyzer

AI驱动的综合健康分析系统,整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。

48

Quality

54%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

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/ai-analyzer/SKILL.md

The canonical home for this skill is ai-analyzer in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

50%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 a clear nine-step sequence and concrete data file paths, but suffers from duplicated risk/algorithm descriptions, pseudocode instead of executable code, and no validation checkpoints for batch/write operations. Progressive disclosure is weak because all detail is inlined and the referenced script is absent.

Suggestions

Add explicit validation/verification checkpoints (e.g., confirm data files exist and are non-empty before analysis, validate report output before writing history) to lift workflow clarity above the 3 cap.

De-duplicate the risk-model and algorithm descriptions so each appears in one canonical section, with the other sections cross-referencing it, to improve conciseness.

Replace pseudocode readFile()/exists() blocks with concrete, executable guidance (actual tool invocations or real script calls) and provide a minimal runnable example for the core analysis.

DimensionReasoningScore

Conciseness

Risk models and algorithms (Framingham, ADA, ASCVD, CUSUM, Z-score) are restated across '核心功能', '执行步骤', '算法说明', and the data-source table, creating avoidable duplication; below 4 because tightening is clearly possible, above 2 because there is no concept-over-explanation padding.

3 / 5

Actionability

Concrete file paths (data/profile.json, data-example/*.json) and a named script appear, but the readFile()/exists() blocks are pseudocode and analysis steps (步骤4-7) are abstract ('进行数据清洗、时间对齐') rather than executable guidance.

3 / 5

Workflow Clarity

Nine steps are clearly sequenced, but batch data reading and report/history writing lack any validation or verification checkpoint; per the guidelines this caps workflow_clarity at 3.

3 / 5

Progressive Disclosure

No bundle files exist and all content (algorithms, data-source table, command reference) is inlined in SKILL.md with header-based structure only; the referenced scripts/generate_ai_report.py is not present in the bundle.

3 / 5

Total

12

/

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 clearly conveys what the skill does with several concrete capabilities, but lacks an explicit 'Use when...' trigger clause, capping completeness at 3. Trigger terms are relevant but not framed as natural user phrases.

Suggestions

Add an explicit 'Use when...' clause naming natural user triggers (e.g., 'Use when the user asks to analyze their health data, predict disease risk, or generate a health report').

Include common synonyms and concrete trigger phrases users would actually say (e.g., '健康风险评估', '生成健康报告', '分析我的睡眠/运动数据') to lift trigger term quality.

Tighten capability wording from abstract verbs to more concrete operations to push specificity toward 5.

DimensionReasoningScore

Specificity

Lists several concrete actions ('整合多维度健康数据', '识别异常模式', '预测健康风险', '提供个性化建议', '智能问答', 'AI健康报告生成'), matching anchor 4; not a 5 because the actions stay somewhat generic and coverage of how each works is thin.

4 / 5

Completeness

The 'what' is clearly stated, but there is no 'Use when...' clause or equivalent explicit trigger guidance, which per the guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

Domain terms like '健康分析', '健康风险', '智能问答', and 'AI健康报告' are present but framed as capabilities rather than natural user phrases, and synonyms/common variations are missing.

3 / 5

Distinctiveness Conflict Risk

'AI驱动的综合健康分析系统' carves a clear health-analysis niche with only minor overlap risk against related wellness skills; not a 5 because triggers are not explicitly distinguished.

4 / 5

Total

14

/

20

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

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

Warning

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

13

/

16

Passed

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

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.