Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Lylll9436/Paper-Polish-Workflow-skill --skill ppw-polishgit clone --depth 1 https://github.com/Lylll9436/Paper-Polish-Workflow-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lylll9436/paper-polish-workflow-skill/ppw-polish)<a href="https://agentmods.dev/skills/lylll9436/paper-polish-workflow-skill/ppw-polish"><img src="https://agentmods.dev/badge/skills/lylll9436/paper-polish-workflow-skill/ppw-polish/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lylll9436/paper-polish-workflow-skill/ppw-polish"><img src="https://agentmods.dev/badge/skills/lylll9436/paper-polish-workflow-skill/ppw-polish.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.03605 |
| Opus 5 | $0.00021 | $0.01802 |
| Sonnet 5 | $0.00008 | $0.00721 |
| Haiku 4.5 | $0.00004 | $0.00361 |
Grade A, and why
ppw:polish scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 266 lines · 42 tokens per session scan A dfefd9512e9d
ppw:polish is a skill published in the GitHub repository Lylll9436/Paper-Polish-Workflow-skill (386 stars, last pushed 5mo ago), licensed MIT. It adds 42 tokens to every session and 3,605 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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