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style-extractor

所属包: petfish

Extract personal writing style from samples to create a style profile. Analyzes sentence patterns, vocabulary preferences, argumentation structure, punctuation habits, tone markers, and AI-distinguishability signals across Chinese and English. Use when "提炼我的写作风格", "extract my style", "create style profile", "analyze my writing", "风格画像", "个性化风格提取", or when setting up petfish-style-rewriter for the first time.

兼容性: opencode

Style Extractor

Purpose

Transform a user's writing samples into a structured, actionable style profile. The profile captures the user's recognizably-human voice across sentence-level, paragraph-level, vocabulary-level, and argumentation-level signals. The output is consumed by petfish-style-rewriter so that rewrites match the user's style rather than a generic target.

This skill is extraction only. It does not detect AI-generated text and it does not rewrite content.

Triggers / Activation

Use this skill when the user asks for any of the following:

  • 提炼我的写作风格 / 提炼写作风格
  • extract my style / extract my writing style
  • create style profile / build a style profile
  • analyze my writing / analyze my style
  • 风格画像 / 我的风格画像
  • 个性化风格提取 / 个性化设置
  • 第一次设置 petfish-style-rewriter
  • first-time setup for style-rewriter

Do not use this skill for:

  • Direct rewriting or polishing → route to petfish-style-rewriter
  • Generic AI-text detection → not supported
  • Document conversion only → route to doc-reader

Prerequisites

  1. The user must provide 3 or more writing samples.
  2. More samples produce a better extraction.
  3. Minimum bilingual requirement: if the user writes in both Chinese and English, provide at least 1 Chinese + 1 English sample.
  4. Accepted formats: Markdown (.md), DOCX (.docx), PDF (.pdf).
  5. DOCX and PDF must first be converted to Markdown via doc-reader (scripts/doc_to_markdown.py) before analysis.

Domain Rules

  • This skill never writes, edits, or rewrites the user's text. It only reads and measures.
  • The final style profile is written to .petfish/style-profile.md in the active project, not inside the skill pack. Style profiles are private user data.
  • Quantitative metrics are produced by a stdlib-only Python script (scripts/analyze_style.py). Qualitative synthesis is performed by the LLM using those metrics as evidence.
  • Chinese and English are analyzed separately and then cross-compared. Do not collapse them into one undifferentiated profile.
  • Signature phrases, pronoun preferences, and register patterns must be derived from the data, not invented.
  • "AI-distinguishability signals" are positive human markers (burstiness, idiosyncrasy, imperfection) that should be preserved, not eliminated.

Extraction Dimensions

Based on nuwa-skill's multi-dimensional extraction methodology:

  1. Sentence statistics — length, variance, complexity, burstiness (coefficient of variation).
  2. Vocabulary profile — density, formality tier, signature phrases, technical density.
  3. Argumentation pattern — how the user builds and closes arguments.
  4. Transition style — connector preferences and logical-flow markers.
  5. Paragraph organization — length, structure, topic-sentence position.
  6. Punctuation habits — comma-heavy vs period-heavy, semicolon/colon/dash usage.
  7. Title/heading conventions — heading depth, numbering style, capitalization.
  8. Opening/closing patterns — how pieces begin and end.
  9. Person pronoun usage — 笔者/我们/本文/我 for Chinese; we/I/this paper for English.
  10. Register awareness — formal vs casual, code-switching, domain tone.
  11. AI distinguishability — what makes the writing recognizably human and should be preserved.

See references/extraction-dimensions.md for detailed definitions, interpretation guidance, and example profiles.

Two-Stage Workflow

Stage 0: Convert non-Markdown samples

For each PDF or DOCX sample, run:

uv run scripts/doc_to_markdown.py sample.docx --output sample.md
uv run scripts/doc_to_markdown.py sample.pdf --output sample.md

Stage 1: Quantitative analysis

Run the analyzer on the directory of Markdown samples:

uv run .opencode/skills/style-extractor/scripts/analyze_style.py --samples ./my-samples/ --output style-metrics.json

... (完整 SKILL.md 中还有 146 行)