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content-humanizer

分析一段内容或一个文件的"人味"(即像不像人类自然写出来的,AI 生成痕迹有多重),给出 0-100 量化打分,并在低于目标线(默认 80 分,用户可指定其他数值如90分)时在不改变原意的前提下改写到目标分数以上。触发条件:用户提到"人味"、"AI味"、"AI感"、"降AI率"、"去AI化"、"人性化改写"、"像人写的"、"过人工检测"、"过AI检测"、"让文章更自然"、"这段话是不是AI写的"、"帮我把这篇文章改得不像AI写的",或者给出一段文字/文件并希望知道"读起来自然不自然"、"会不会被认出是AI写的"。即使用户只说"这段话感觉很机器,帮我改一下"或"帮我看看这篇稿子像不像真人写的",也应使用本 skill。输出为 Markdown 报告,保存到当前项目 markdown/ 目录。

74

Quality

91%

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SecuritybySnyk

High

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SKILL.md
Quality
Evals
Security

Quality

Content

92%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.

A well-structured multi-step skill: every step is executable, the rewrite loop re-validates with the same objective script and caps iterations with honest failure reporting, and detail is properly pushed to the two real bundle files. The only trimmable material is the design-rationale prose in the opening section and the threshold-semantics paragraph in step 4.

Suggestions

Condense the opening "为什么这么设计" section to 2-3 lines — the two-layer rationale is largely re-derivable from steps 2-4.

Move the step-4 discussion of what the 80-point threshold does and does not guarantee into a short note; keep the operational rule (default 80, user override) up front.

DimensionReasoningScore

Conciseness

Mostly efficient: tight step structure, concrete tables, and explicit restraint ("具体方法见脚本内注释,不用解释给用户听"). Not 5 because the opening design-rationale section and the step-4 threshold-semantics paragraph are justifications that could be trimmed without losing executability. Not 3: no explanations of concepts Claude already knows; padding is minor.

4 / 5

Actionability

Fully executable guidance: copy-paste script invocations for both file and inline-text input, a concrete per-dimension table of what to look for, a verbatim report template, and specific rewrite tactics with before/after examples ("综上所述,这是一个值得关注的趋势" → 直接给结论). Matches the copy-paste-ready anchor.

5 / 5

Workflow Clarity

Steps 1–6 are clearly sequenced with an explicit validation feedback loop: "重新跑一遍第二步的脚本 + 第三步的人工评分…最多改 3 轮", plus honest-failure handling ("不要硬编数据把分数做上去") and edge cases covering batch multi-file input. Matches the explicit-validation-with-error-recovery anchor.

5 / 5

Progressive Disclosure

The body stays an overview and offloads detail one level deep to real, clearly signaled files: "详细的打分锚点…见 references/rubric.md" (exists, 66 lines) and the scoring script (exists, 229 lines). The inline report template is required output spec, appropriately inline. Clear navigation with no nested references.

5 / 5

Total

19

/

20

Passed

Description

91%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.

A strong description that clearly states what the skill does, when to use it via an extensive and natural trigger list, and what output to expect. Third-person voice is used throughout and capabilities are stated concretely with quantified thresholds. The only weaknesses are minor: input-format handling and the scoring method are left to the body, and a few broader triggers slightly overlap with generic editing requests.

Suggestions

Briefly mention accepted input formats in the description (e.g. pasted text or plain-text/Markdown/docx/pdf files) so the capability scope is fully stated up front.

Narrow the broadest triggers ("让文章更自然") by pairing them with AI-detection context so they don't compete with generic editing/polishing skills.

DimensionReasoningScore

Specificity

Names concrete actions with quantified parameters — "给出 0-100 量化打分", "在不改变原意的前提下改写到目标分数以上", "输出为 Markdown 报告,保存到当前项目 markdown/ 目录". Falls short of the comprehensive anchor because supported input formats (docx/pdf/pptx) and the two-layer scoring method are only covered in the body, leaving minor gaps.

4 / 5

Completeness

Explicitly answers both what ("分析…给出 0-100 量化打分…改写到目标分数以上…输出为 Markdown 报告") and when (a dedicated "触发条件:" clause listing concrete user phrases). Exactly the both-what-and-when-with-concrete-triggers anchor.

5 / 5

Trigger Term Quality

Comprehensive natural trigger phrases with synonyms: "人味", "AI味", "AI感", "降AI率", "去AI化", "人性化改写", "过AI检测", "这段话是不是AI写的", plus indirect phrasings like "这段话感觉很机器,帮我改一下". Matches the comprehensive-coverage-with-synonyms anchor.

5 / 5

Distinctiveness Conflict Risk

Clear niche (AI-trace quantification and humanization) with distinct triggers like "降AI率" and "去AI化". Minor overlap risk remains: broader phrases such as "让文章更自然" could also match generic editing/polishing skills.

4 / 5

Total

18

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
digoal/blog
Reviewed

Table of Contents

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