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writing-anti-ai

This skill should be used when the user asks to "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing", or needs to eliminate AI writing patterns from prose. Supports both English and Chinese text. Based on Wikipedia's "Signs of AI writing" guide, detects and fixes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, AI vocabulary, negative parallelisms, and excessive conjunctive phrases.

67

Quality

81%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

67%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-organized bilingual editing skill with genuinely actionable pattern tables, rewrite pairs, and a scoring rubric. Its main problems are redundancy (three overlapping pattern/fix tables, duplicate resource listings, an unneeded explanation of how LLMs work) and a broken progressive-disclosure promise: the examples/ files referenced twice do not exist, and the Quick Scoring checkpoint is not integrated into the stated workflow.

Suggestions

Create examples/english.md and examples/chinese.md or remove the two sections that reference them — the Examples (示例) and Example Files entries currently point at files that do not exist in the bundle.

Deduplicate the pattern guidance: merge the Quick Reference fixes table and the Common AI Patterns tables into the Core Rules (or move them into references/), and list each reference/example file once instead of twice.

Integrate the Quick Scoring rubric into the Workflow as an explicit final step (e.g., '6. Score the result; below 35 → revise') and drop the 'Core insight' paragraph explaining LLM statistical prediction, which covers knowledge Claude already has.

DimensionReasoningScore

Conciseness

Mostly efficient (dense before/after tables, minimal prose) but padded by duplication and one unneeded concept explanation: the "Core insight" paragraph explains how LLM statistical prediction works (something Claude already knows), the pattern tables appear in three overlapping forms (Core Rules, Common AI Patterns, Quick Reference), and the references/ and examples/ file lists are each repeated in two sections. Not 4 because the duplication is more than minor trimming; not 2 because tables are token-dense and the bilingual repetition is functionally justified.

3 / 5

Actionability

Concrete, executable editing guidance throughout: rewrite pairs ("In order to achieve this goal" → "To achieve this", "serves as a testament to" → "shows"), words-to-watch vocabulary lists in both languages, bad/good contrast examples, and a measurable scoring rubric. For an instruction-only skill this is mostly copy-paste-ready; the gap keeping it from 5 is that the "Examples (示例)" section points to examples/english.md and examples/chinese.md, which do not exist in the bundle, so the promised before/after transformations are unavailable.

4 / 5

Workflow Clarity

A clear 5-step sequence (identify patterns → rewrite sections → preserve meaning → maintain voice → add soul) is provided in both English and Chinese, and the separate "Quick Scoring" section supplies a measurable standard (45-50 excellent, below 35 needs revision). Not 5 because the scoring checkpoint is never wired into the workflow as an explicit validate/re-vise feedback loop — it reads as an appendix rather than step 6.

4 / 5

Progressive Disclosure

Good structure against the actual bundle: overview and core rules inline, full detail correctly split into four real one-level-deep files (references/patterns-english.md, patterns-chinese.md, phrases-to-cut.md, wikipedia-source.md) with descriptive labels. Not 5 because two of the six referenced paths — examples/english.md and examples/chinese.md, cited in both the Examples and Additional Resources sections — do not exist in the bundle, so navigation breaks for the examples material. Not 3 because the references that do exist are clearly signaled and nothing is improperly inlined.

4 / 5

Total

15

/

20

Passed

Description

96%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: it states concrete detection/fix actions, enumerates the exact pattern types targeted, declares English and Chinese support, and gives an explicit 'should be used when' clause with five natural quoted trigger phrases. The only weakness is mild overlap risk between general trigger phrases like "make this sound more natural" and generic editing/polishing skills.

DimensionReasoningScore

Specificity

Concrete actions are explicit: "detects and fixes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, AI vocabulary, negative parallelisms, and excessive conjunctive phrases" plus "eliminate AI writing patterns from prose", with the bilingual scope stated. Multiple specific actions with comprehensive coverage of the pattern types handled; not the 4 anchor because no meaningful coverage gaps remain.

5 / 5

Completeness

Explicitly answers both questions: what ("detects and fixes inflated symbolism, promotional language...") and when ("This skill should be used when the user asks to... or needs to eliminate AI writing patterns from prose") with concrete trigger phrases. Matches the 5 anchor's structure directly; a 'Use when' clause with trigger phrases is present, so the completeness cap does not apply.

5 / 5

Trigger Term Quality

Five quoted natural user phrases — "remove AI writing patterns", "humanize this text", "make this sound more natural", "remove AI-generated traces", "fix robotic writing" — plus the paraphrase "needs to eliminate AI writing patterns from prose". These are exactly the phrases a user would say, covering synonyms and variations; nothing common is missing.

5 / 5

Distinctiveness Conflict Risk

The anti-AI-writing niche is clear and the triggers are distinct ("humanize this text", "remove AI-generated traces"), but phrases like "make this sound more natural" overlap with general editing/polishing skills, so minor overlap risk with closely related skills remains. Not 5: not minimal conflict risk; not 3: the domain is far more specific than generic document handling.

4 / 5

Total

19

/

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.

Validation — 15 / 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
Galaxy-Dawn/claude-scholar
Reviewed

Table of Contents

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