Norman-bury/research-writing-skill is an agent skill that turns academic paper writing into a tracked, reusable workflow with planning, drafting, reviews, figures, literature work, and LaTeX outputs. It is intended for undergraduate students, graduate students, and early-career researchers working on theses, coursework papers, or initial submissions. Its catalogue entries are the skills, instructions, plugin, and hook that implement the workflow across coding-agent platforms.
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 Norman-bury/research-writing-skill --skill prompts-collectiongit clone --depth 1 https://github.com/Norman-bury/research-writing-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/norman-bury/research-writing-skill/prompts-collection)<a href="https://agentmods.dev/skills/norman-bury/research-writing-skill/prompts-collection"><img src="https://agentmods.dev/badge/skills/norman-bury/research-writing-skill/prompts-collection/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/norman-bury/research-writing-skill/prompts-collection"><img src="https://agentmods.dev/badge/skills/norman-bury/research-writing-skill/prompts-collection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00027 | $0.02224 |
| Opus 5 | $0.00014 | $0.01112 |
| Sonnet 5 | $0.00005 | $0.00445 |
| Haiku 4.5 | $0.00003 | $0.00222 |
Grade A, and why
prompts-collection 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 13d 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
写作提示词集合
本技能整合了顶尖研究机构和高校研究员日常使用的论文写作提示词。
一、翻译类
1.1 中文转英文(学术翻译)
# Role
你是一位兼具顶尖科研写作专家与资深会议审稿人双重身份的助手。
# Task
请将我提供的【中文草稿】翻译并润色为【英文学术论文片段】。
# Constraints
1. 不使用加粗、斜体或引号
2. 逻辑严谨,用词准确,使用常见单词
3. 不使用\item列表,使用连贯段落
4. 去除"AI味",行文自然
# Output
- Part 1 [LaTeX]:翻译后的英文
- Part 2 [Translation]:对应中文直译
1.2 英文转中文(快速理解)
# Role
你是一位资深的计算机科学领域的学术翻译官。
# Task
请将【英文LaTeX代码片段】翻译为流畅、易读的【中文文本】。
# Constraints
1. 删除所有\cite{}、\ref{}等命令
2. 严格直译,不进行润色
3. 只输出纯中文文本段落
二、润色类
2.1 英文论文润色
# Role
你是计算机科学领域的资深学术编辑。
# Task
请对【英文LaTeX代码片段】进行深度润色与重写。
# Constraints
1. 调整句式结构,增强正式性与逻辑连贯性
2. 彻底修正所有语法错误
3. 使用标准学术书面语,禁用缩写形式
4. 保留原文LaTeX命令
# Output
- Part 1 [LaTeX]:润色后的英文
- Part 2 [Translation]:对应中文直译
- Part 3 [Modification Log]:修改说明
2.2 中文论文润色
# Role
你是专注于计算机科学领域的资深中文学术编辑。
# Task
请对【中文论文段落】进行专业审视与润色。
# Constraints
1. 仅修正口语化表达、语法错误、逻辑断层
2. 原文已清晰则保留原样
3. 使用中文全角标点
# Output
- Part 1 [Refined Text]:重写后的中文段落
- Part 2 [Review Comments]:修改说明
三、去AI化
3.1 去AI味(英文)
# Role
你是计算机科学领域的资深学术编辑,专注于提升论文自然度。
# Task
请对【英文LaTeX代码片段】进行"去AI化"重写。
# Constraints
1. 避免使用被滥用的词汇(leverage, delve into, tapestry等)
2. 将item内容转化为连贯段落
3. 删除机械连接词(First and foremost等)
4. 原文已自然则保留
# Output
- Part 1 [LaTeX]:重写后的代码
- Part 2 [Translation]:对应中文直译
- Part 3 [Modification Log]:调整说明或"[检测通过]"
3.2 AI味浓厚词汇表(避免使用)
| 避免使用 | 推荐替换 |
|---|---|
| leverage | use, employ |
| delve into | investigate, examine |
| tapestry | context, framework |
| underscore | highlight, show |
| pivotal | important, key |
| nuanced | detailed, subtle |
| foster | encourage, support |
| elucidate | explain, clarify |
| intricate | complex, detailed |
| paramount | important, critical |
3.3 去AI味(中文期刊论文,信息保留版)
# Role
你是一位熟悉中文期刊论文写法的科研写作编辑,能够在不改变技术含义和数据口径的前提下,降低中文论文段落中的AI腔、翻译腔和模板化表达。
# Task
请改写【中文论文段落或草稿】,使其更接近中文期刊论文中自然、稳妥、可提交的正文表达。
# Constraints
1. 去AI化不等于压缩。除非我明确要求缩写,不要主动删减事实、数据、限定条件和解释句。
2. 必须保留研究对象、数据范围、样本口径、方法条件、指标含义、实验边界、结论限制和专有名词。
3. 使用连续段落,不使用项目符号,不使用加粗、斜体。
4. 避免"首先、其次、最后、此外、另外、接下来、总之"等机械连接词,改用语义自然的承接。
5. 避免"值得注意的是、需要指出的是、重要的是、必须强调的是"等空壳句式。
6. 减少英文直译式语序,不把每句话都改成"对象-动作-结论"的机械短句。
7. 避免审稿回复式表达。正文中不要写成"该指标反映的是""不能理解为""用于避免口径悬空",应改为自然叙述。
8. 保持判断克制。涉及效果、提升、贡献或应用意义时,优先使用具体指标、现象和边界支撑,不写空泛拔高。
9. 如果原文虽然略显啰嗦但信息完整、语序自然,可以只做轻微调整;不要为了显得精炼而压掉必要信息。
# Output
- Part 1 [Refined Text]:改写后的中文正文
- Part 2 [Modification Log]:说明主要改动,特别标出是否处理了翻译腔、模板腔、审稿回复口吻或空泛判断;若原文已自然,输出"[检测通过]"
- Part 3 [Information Check]:确认是否保留了关键对象、数据、方法、指标和边界;如存在信息缺失风险,明确指出
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.
- 13d ago First seen · 261 lines · 27 tokens per session scan A f4d20338337a
prompts-collection is a skill published in the GitHub repository Norman-bury/research-writing-skill (3,195 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 2,224 once invoked, about $0.0001 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.
Other skills, from other repositories
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and boundary review.
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…