Remove Ai Style

作者 zc277584121959680e17acf无许可证收录于 2026年10月8日更新于 2026年10月8日仓库5周前更新

Aggressively rewrite Chinese or English prose to remove AI-generated patterns. Use for de-AI polishing, natural-language rewrites, robotic or formulaic writing, and publication cleanup. Always run the bundled deterministic Markdown analyzer first, remove every editable exclamation mark, dash, and formulaic binary contrast, inspect every other reported location, then read the full article for semantic patterns the rules cannot enumerate.

包含脚本Writing & Content
AI 生成的概览

重写中文或英文文章以去除 AI 生成痕迹,结合内置分析脚本与人工语义通读。

功能
该技能先用内置 Python 脚本扫描 Markdown 文章,标记词汇、标点、结构和助手残留等可重复信号,再引导完整通读以发现规则无法覆盖的模式。它会改写文本,删除感叹号、破折号和公式化的二元对比,并清除元叙述、空洞评价形容词和刻意节奏,同时保留事实、结构与作者语气。产出为修改后的文件以及包含修改前后问题计数和硬性约束检查结果的变更报告。
适用场景
适用于中文或英文文章的“去 AI 味”润色、把机械或套路化文字改写得更自然,以及发布前的清理。当 Markdown 草稿既需要规则检测又需要对语气和结构做语境判断时使用。
运行要求
需要 Python 3 运行内置脚本 scripts/analyze_ai_style.py,并需要参考文件 references/chinese.md 与 references/english.md。输入为 Markdown 文件路径或通过标准输入传入文本;未提及凭据或网络访问。

Remove AI Style

Use a two-layer workflow:

  1. Run the deterministic analyzer to locate repeatable lexical, punctuation, structural, and assistant-residue signals.
  2. Read the full article and make contextual judgments that rules cannot cover.

Apply one strong default. Fix confirmed findings at every severity, rewrite awkward passages, and remove unflagged formulaic rhythm while preserving facts, meaning, required structure, and the author's intended voice. Do not ask the user to choose an intensity and do not offer light, moderate, heavy, or full modes.

Most analyzer hits still require contextual judgment. Three rule families are hard publication constraints in editable prose:

  • Remove all exclamation marks.
  • Remove all em dashes, en dashes, and double hyphens used as dashes.
  • Rewrite all formulaic binary contrasts, including “不是……而是……”, “并非……更是……”, “看似……其实……”, “not just X but Y”, and close variants, as direct claims.

The post-edit analyzer counts for zh-exclamation or en-exclamation, zh-dash or en-dash, and zh-binary-contrast or en-binary-contrast must be zero. The only exceptions are protected regions such as code, URLs, Markdown targets, and source text that must remain verbatim. Do not treat brand enthusiasm, casual tone, or a writer's habitual punctuation as reasons to keep these patterns.

Required workflow

1. Determine input and language

Prefer an absolute Markdown file path. If the user provides text only, pass it to the analyzer through stdin.

Detect Chinese or English from the article. Override auto-detection only when the result is wrong or the user explicitly specifies a language.

Load the matching reference completely:

  • Chinese: references/chinese.md [blocked]
  • English: references/english.md [blocked]

2. Run the deterministic analyzer

Run before editing:

bash
python3 <skill-root>/scripts/analyze_ai_style.py \  <article-path> \  --language auto \  --format json

The report contains:

  • stable finding ID
  • rule and category
  • severity
  • line and column
  • matched text
  • local context
  • suggested treatment
  • repeated-rule and document-structure summaries

Do not replace this command with copied grep snippets. The bundled script is the single owner of deterministic detection logic.

3. Inspect every finding

Rewrite every editable finding from the hard-constraint rule families. Do not classify these findings as intentional or false positives merely because the usage is grammatically valid.

Classify every other reported item as one of:

  • confirmed: rewrite it
  • intentional: keep it because the genre or voice requires it
  • protected: leave it because it belongs to code, source material, structure, or another preserved region
  • false_positive: no change
  • semantic_review: the local hit is real, but the correct fix depends on wider context

Read the reported context, then inspect the surrounding paragraph before deciding. Do not bulk-replace phrases across the document.

4. Read the full original article

Always read the complete article unless the user explicitly asks to process only an excerpt. The analyzer cannot reliably detect:

  • excessive metaphors with novel wording
  • argument structure that is too symmetrical
  • repeated paragraph logic with different vocabulary
  • synthetic emotional escalation
  • vague claims that sound polished but say little
  • reader-mind-reading and sentences that digest the point for the reader
  • empty evaluative adjectives such as “很清晰” or “很重要”
  • claims that announce importance without a mechanism, number, or example
  • genre mismatch
  • flattened author voice
  • suspicious facts or citations

Use the language reference to perform this semantic pass. The report is a navigation aid, not a substitute for reading.

5. Record protected structure

Before editing, record the structures that must survive:

  • YAML frontmatter
  • fenced code blocks
  • Markdown tables when their structure is intentional
  • images and links
  • HTML comments
  • complete VISUAL_TODO blocks
  • quoted source material and citations

Do not change facts, numbers, URLs, code, image paths, TODO IDs, or citation targets merely to make prose sound more natural.

6. Rewrite aggressively

Make contextual edits rather than phrase substitution:

  • Prefer concrete subjects, actions, mechanisms, and results. Lead with the fact, then the judgment; do not add emphasis that the evidence already carries.
  • Remove meta-writing, reader-mind-reading, generic importance claims, empty evaluative adjectives, assistant residue, and decorative transitions.
  • Vary rhythm only where the current rhythm feels artificial; do not add slang, mistakes, or random fragments to imitate a human.
  • Preserve deliberate voice, technical precision, and genre-appropriate structure. Judgments may sit next to narrative, but each judgment should rest on a fact or mechanism.
  • Drive confirmed findings down as far as the material allows. The hard-constraint rule counts must reach zero outside protected regions.

When invoked as a Subagent on a file, edit the file directly, then return a concise change report. Do not delegate the rewrite to another agent.

7. Run the analyzer again

After editing, rerun the same command and compare before/after summaries.

Use the hard-constraint gate for the final rescan:

bash
python3 <skill-root>/scripts/analyze_ai_style.py \  <article-path> \  --language auto \  --format json \  --fail-on-hard-constraints

Exit status 2 means at least one exclamation mark, dash, or formulaic binary contrast remains in scanned prose. Rewrite it unless inspection confirms that the match belongs to source text that must remain verbatim.

Review every remaining finding individually. A remaining non-hard-constraint hit is acceptable when it is intentional, protected, required for accuracy, or a documented false positive. A remaining hard-constraint hit is acceptable only when it is protected or must remain verbatim.

Finally, read the full revised article once more for continuity and voice. A lower finding count does not prove the rewrite is good.

Completion report

Return:

  • language
  • before/after finding counts by severity
  • confirmation that the hard-constraint rule counts reached zero, or an exact list of protected verbatim exceptions
  • confirmed rules addressed
  • intentional or protected findings left in place
  • important semantic changes found only by full reading
  • confirmation that protected Markdown structures survived
  • any factual or citation issue that needs human verification

来源与署名

来源:zc277584121/marketing-skills位于skills/remove-ai-style提交959680e

许可证: 无许可证

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