scientific-writing-wrapper
AI-powered scientific writing workflow from outline to polished draft
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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AI-powered scientific writing workflow from outline to polished draft
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
公司金融实证研究的"漏斗式选题查找器"。互动开场先后询问 (1) 研究方向、(2) 候选标题数量 N, 再扫描全球文献(已出版英文学术期刊 + SSRN working paper + 全球高校 department seminar 1 年内日程),基于 Edmans (2024) "1000 Rejections" 红线生成 N 个候选标题,**通过并行 subagent(Agent 工具)批量生成计划书 + 查新;每个 subagent 必须强制调用 Skill 工具加载 econfin-proposal 与 novelty-check 两个预设 skill 完成各自模块**,**只有当 novelty score >= 9 时(即 JF/JFE/RFS 顶刊层次),subagent 才把 proposal + 查新报告合并的 md 写入 F:\Dropbox\CC\选题大全\<研究方向短名>\(以"简短选题名称-分数"命名,子文件夹名由 Step 0 从用户输入的研究方向派生);< 9 分的选题在 subagent 内部直接丢弃,绝不写盘、绝不输出**。当用户说"找选题"、"帮我找选题"、"想做 X 方向"、 "empirical CF idea search"、"批量生成研究计划书"、"100 ideas"、"econfin-idea-finder" 时触发。
Create and compile beautiful Beamer presentations following the Rhetoric of Decks philosophy. Use when making slides, creating decks, or compiling .tex presentation files.
Scaffold a new research project with standard directory structure, CLAUDE.md template, and documented README. Use this at the start of every new project to ensure consistent organization.
Download, split, and deeply read academic PDFs. Use when asked to read, review, or summarize an academic paper. Splits PDFs into 4-page chunks, reads them in small batches, and produces structured reading notes — avoiding context window crashes and shallow comprehension.
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
| name | scientific-writing-wrapper |
| description | AI-powered scientific writing workflow from outline to polished draft |
| metadata | {"openclaw":{"emoji":"✍️","category":"writing","subcategory":"composition","keywords":["scientific writing","AI writing","draft generation","writing workflow","paper composition","manuscript"],"source":"wentor-research-plugins"}} |
Writing a scientific paper is one of the most time-consuming parts of the research process. The Scientific Writing Wrapper skill provides a structured, AI-assisted workflow that takes you from raw research notes and results to a polished manuscript draft. It is not about generating papers from nothing—it is about using AI tools strategically to accelerate each phase of the writing process while maintaining scientific rigor and your own voice.
This skill covers the complete writing lifecycle: outlining, drafting section by section, iterative revision, and final polish. At each stage, it provides specific prompting strategies and quality checkpoints that ensure the AI output meets the standards of peer-reviewed publication. The workflow is designed to keep you in control of the scientific content while delegating the mechanical aspects of prose generation.
The approach is venue-agnostic and works for journal articles, conference papers, and technical reports across all scientific disciplines.
Before generating any prose, assemble your writing inputs:
Create a structured writing folder with the following:
manuscript/
notes/
research_question.md # Your RQ, hypotheses, and scope
key_findings.md # Bullet-point list of all results
figure_descriptions.md # Description of each figure/table and what it shows
related_work_notes.md # Notes on how your work relates to prior literature
data/
results_tables.csv # Raw data for results tables
figures/ # All figure files
references/
bibliography.bib # BibTeX file with all references
drafts/ # Generated drafts will go here
Write a detailed outline before generating any prose:
This outline is your contract with yourself about what the paper will say. AI-generated prose should fill in this structure, not change it.
Draft each section independently, working from the most concrete (Methods, Results) to the most interpretive (Discussion, Introduction).
When using an LLM to draft sections, provide rich context:
I am writing the Methods section of a paper about [topic] for [venue].
Here is my outline for this section:
[paste outline]
Here are my detailed notes:
[paste relevant notes]
Please draft this section following these guidelines:
- Use passive voice sparingly; prefer active voice
- Be precise about sample sizes, parameters, and tools
- Include enough detail for reproducibility
- Target approximately [N] words
- Use past tense for describing what was done
- Do not invent any details not present in my notes
After generating a draft of any section, review it against these criteria before moving on:
Once all sections are drafted individually, assemble them into a complete manuscript and perform an integration pass:
Use targeted revision prompts for specific improvements:
Before submitting for peer review or to a journal, verify: