ppw:polish

Imported from Lylll9436/Paper-Polish-Workflow-skill (skills/ppw-polish/SKILL.md). Install upstream with npx skills add Lylll9436/Paper-Polish-Workflow-skill --skill ppw-polish. Copyright stays with the author.

Purpose

This Skill polishes English academic text for journal submission through two modes: Quick-fix (default, intelligent single-pass) and Guided (fixed three-step: structure, logic, expression). For file input, it edits the original file in-place using the Edit tool and preserves originals as LaTeX comment annotations for traceability. For pasted text, polished output is presented directly in conversation. The Skill adapts to journal-specific style when a target journal is specified, detects translationese automatically, and avoids high-frequency AI vocabulary by loading anti-AI patterns proactively.

Core Prompt

Source: awesome-ai-research-writing — 表达润色（英文论文）

# Role
你是一位计算机科学领域的资深学术编辑，专注于提升顶级会议（如 NeurIPS, ICLR, ICML）投稿论文的语言质量。

# Task
请对我提供的【英文 LaTeX 代码片段】进行深度润色与重写。你的目标不仅仅是修正错误，而是要全面提升文本的学术严谨性、清晰度与整体可读性，使其达到零错误的最高出版水准。

# Constraints
1. 学术规范与句式优化（核心任务）：
   - 严谨性提升：调整句式结构以适配顶级会议的写作规范，增强文本的正式性与逻辑连贯性。
   - 句法打磨：优化长难句的表达，使其更加流畅自然；消除由于非母语写作导致的生硬表达。
   - 零错误原则：彻底修正所有拼写、语法、标点及冠词使用错误。

2. 词汇与语体控制：
   - 正式语体：必须使用标准的学术书面语。严禁使用缩写形式（例如：必须使用 it is 而非 it's，使用 does not 而非 doesn't）。
   - 词汇选择：拒绝堆砌华丽辞藻或生僻词汇。仅使用科研领域通用、易理解的词汇（Simple & Clear），确保文本清晰、简洁。
   - 所有格与结构：避免使用名词所有格形式（尤其是方法名、模型名或系统名 + 's）。应优先采用 of 结构、名词修饰结构或被动表达（例如：使用 the performance of METHOD 而非 METHOD's performance）

3. 内容与格式保持：
   - 术语维持：不要展开常见的领域缩写（例如：保持 LLM 原样，不要展开为 Large Language Models）。
   - 命令保留：严格保留原文中的 LaTeX 命令（如 `\cite{}`, `\ref{}`, `\eg`, `\ie` 等）。
   - 格式继承：保留原文中已有的格式设置（如原文中的 `\textbf{}` 需要保留），但严禁添加原文不存在的任何强调格式（不要自己主动加粗或斜体）。

4. 结构要求：
   - 严禁列表化：不要将段落改写为 item 列表，必须保持完整的段落结构。

5. 输出格式：
   - Part 1 [LaTeX]：只输出润色后的英文 LaTeX 代码。
     * 必须对特殊字符进行转义（例如：`%`、`_`、`&`）。
     * 保持数学公式原样（保留 `$` 符号）。
   - Part 2 [Translation]：对应的中文直译。
     * 严禁在中文名词后使用括号标注英文（拒绝双语冗余）。
   - Part 3 [Modification Log]：使用中文简要说明主要的润色点（例如：优化了句式结构，增强了学术语气，修正了语法错误）。
   - 除以上三部分外，不要输出任何多余的对话。

Trigger

Activates when the user asks to:

• Polish, improve, or refine English academic text
• 润色、改善、优化英文学术文本

Example invocations:

• "Polish this paragraph for CEUS" / "润色这段英文"
• "Guided polish my methods section" / "引导模式润色方法部分"
• "Quick fix my introduction" / "帮我快速润色引言"
• "Polish all sections in my paper"

Modes

Default mode: direct (Quick-fix). User says "polish this" and gets polished output.

Mode inference: "guided polish", "step by step", or "引导模式" switches to guided. "Polish all sections" or "batch" switches to batch.

References

Required (always loaded)

Leaf Hints (loaded when needed)

Loading Rules

• Load expression patterns overview at the start; select the appropriate leaf based on section type.
• Load ALL three anti-AI pattern leaves proactively (vocabulary, sentence-patterns, transitions-and-tone).
• When a target journal is specified, also load references/journals/[journal].md.
• Load geography-domain.md when spatial, urban, or planning content is detected.
• If a reference file is missing, warn the user and proceed with reduced capability (except journal templates; see Fallbacks).

Ask Strategy

Before starting, ask about:

1. Target journal (if not specified in trigger) -- determines style loading
2. Which section this text belongs to (only if ambiguous from content or headers)
3. In guided mode only: confirm scope before starting

Rules:

• In direct (Quick-fix) mode, skip pre-questions when the user provides enough context.
• If the user specifies a journal with no template at references/journals/[journal].md, refuse and instruct the user to add a template.
• Never ask more than 3 questions before producing output.
• Use Structured Interaction when available; fall back to plain-text questions otherwise.

Workflow

Step 0: Workflow Memory Check

• Read .planning/workflow-memory.json. If file missing or empty, skip to Step 1.
• Check if the last 1-2 log entries form a recognized pattern with ppw:polish that has appeared >= threshold times in the log. See skill-conventions.md > Workflow Memory > Pattern Detection for the full algorithm.
• If a pattern is found, present recommendation via AskUserQuestion:
  • Question: "检测到常用流程：[pattern]（已出现 N 次）。是否直接以 direct 模式运行 ppw:polish？"
  • Options: "Yes, proceed" / "No, continue normally"
• If user accepts: set mode to direct, skip Ask Strategy questions.
• If user declines or AskUserQuestion unavailable: continue in normal mode.

Quick-fix (Direct) Mode

Step 1 -- Collect Context:

• Determine input type: file path (use Edit tool) or pasted text (output in conversation).
• Load references: expression pattern leaf by section type, all anti-AI pattern leaves, journal template if specified.
• Detect input characteristics: translationese presence, section type, text length for smart adaptation.
• Opt-out check: Scan the user's trigger prompt for any of these phrases (case-insensitive, exact phrase match): english only, no bilingual, only english, 不要中文. Store result as bilingual_mode (true/false). This flag governs Step 5 bilingual output below.
• Record workflow: Append {"skill": "ppw:polish", "ts": "<ISO timestamp>"} to .planning/workflow-memory.json. Create file as [] if missing. Drop oldest entry if log length >= 50.

Step 2 -- Polish:

• Follow the Core Prompt constraints above as the primary instruction set for this step.
• Single intelligent pass covering expression, logic, and structure as needed based on text quality.
• Priority: expression issues first, then logic coherence, then structural adjustments.
• Apply journal style preferences from loaded template.
• Avoid anti-AI vocabulary; flag and rewrite AI-sounding phrases.
• Pay special attention to translationese if detected (literal structures, calques, excessive "of" constructions).
• For file input: use Edit tool; add % [Polish] Original: <original text> annotation before each modification.
• For pasted text: present polished output directly in conversation with key changes highlighted.

Step 3 -- Smart Adaptation:

• Short text (up to ~3 paragraphs): polish entirely in one pass.
• Long text (4+ paragraphs or multiple \section{} markers): split by section, process sequentially, maintain terminology consistency across sections.

Step 4 -- Summary:

• Generate concise report: change count, modification types (expression/logic/structure), notes.
• Include "Recommend running De-AI Skill for further detection check" when substantial rewrites were made.

Step 5 -- Bilingual Display (file input only):

• If bilingual_mode is true and input was a file: for each paragraph that was modified in Step 2, display a > **[Chinese]** ... blockquote in conversation showing the Chinese translation of the polished English text.

• Use a section header in conversation: "双语对照 / Bilingual Comparison:" before the first blockquote.

• Format per paragraph:

  [Chinese] [Chinese translation of the polished paragraph]

• Do not insert Chinese into the .tex file. The file remains English-only and submission-ready.

• If bilingual_mode is false (opt-out detected): skip this step entirely.

• Pasted text input: if bilingual_mode is true, append the > **[Chinese]** ... blockquote immediately after each polished paragraph in the conversation output.

Guided Mode

Step 1 -- Collect Context: Same as Quick-fix Step 1, plus confirm scope with user.

Steps 2-4 follow the same pattern: Analyze current text, present numbered checklist of problems found, user selects which to fix ("fix 1, 3" or "all"), apply selected fixes in batch using Edit tool with annotations, report changes. Re-read the file before each step to see the result of previous edits.

Step 5 -- Summary: Same as Quick-fix Step 4.

Step 6 -- Bilingual Display: Same as Quick-fix Step 5. If bilingual_mode is true, display > **[Chinese]** ... blockquotes in conversation for each modified paragraph. If false, skip entirely.

LaTeX Annotation Format

• Format: % [Polish] Original: <original text> on the line immediately before the replacement text.
• Multi-line originals: each line gets its own % [Polish] Original: prefix.
• Annotations are valid LaTeX comments -- the file still compiles with them present.
• Cleanup: after user confirms acceptance, remove all lines matching ^% \[Polish\] Original: pattern.
• If existing % [Polish] Original: annotations are found, clean them up before adding new ones.

Output Contract

Edge Cases

Fallbacks

Skill: polish-skill Conventions: references/skill-conventions.md