pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
End-to-end statistical writing assistant for LaTeX - draft title/abstract/keywords, expand outlines into sections, audit manuscripts, write reviewer reports and response letters, and scaffold book manuscripts.
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stat-writing --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stat-writing -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stat-writing -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stat-writing -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stat-writing -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stat-writing -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill stat-writing -a windsurf -y
name: stat-writing description: End-to-end statistical writing assistant for LaTeX - draft title/abstract/keywords, expand outlines into sections, audit manuscripts, write reviewer reports and response letters, and scaffold book manuscripts. license: CC0-1.0 metadata: author: stat-writing-one-skill version: "3.0" compatibility: Codex (CLI + IDE). Optional scripts require Python 3.
<!-- ╔══════════════════════════════════════════════════════════════╗ ║ 本文件为开源 Skill 原始文档,收录仅供学习与研究参考 ║ ║ CoPaper.AI 收集整理 | https://copaper.ai ║ ╚══════════════════════════════════════════════════════════════╝
来源仓库: https://github.com/fuhaoda/stats-paper-writing-agent-skills 项目名称: stats-paper-writing-agent-skills 开源协议: MIT License 收录日期: 2026-04-02
声明: 本文件版权归原作者所有。此处收录旨在为社会科学实证研究者 提供 AI Agent Skills 的集中参考。如有侵权,请联系删除。 -->
This is a single "workbench" skill for statistical manuscripts written in LaTeX.
Guidance is split into focused references under `references/`. Deterministic checks live in `scripts/`. Ready-to-use templates live in `assets/`.
Default behavior is journal-agnostic. For Journal of Data Science (JDS), apply the JDS profile:
Use this skill when the user wants to:
1. Generate compliant front matter (title, abstract, keywords). 2. Expand outlines into complete sections in LaTeX. 3. Audit a manuscript for structure, style, references, and reproducibility quality. 4. Draft reviewer reports. 5. Draft point-by-point response letters. 6. Scaffold a book manuscript from a chapter plan.
Prefer file paths over pasted text.
If details are missing, proceed with placeholders like ` odo{...}` and ask only critical questions.
Unless the user requests otherwise:
Open only the reference files needed for the task.
These checks are heuristic and do not compile LaTeX.
1. Read introduction/methods/results/discussion. 2. Use `references/11-abstract.md` and `references/12-keywords.md`. 3. Draft abstract (default 6-8 sentences, acceptable 4-10, no citations, no math notation). 4. Draft 6-10 keywords, alphabetized, avoid repeating title terms. 5. Return:
1. Run `check_tex.py` (and `check_bib.py` if `.bib` exists). 2. Use `references/31-general-style.md`, `references/40-bibtex-natbib.md`, and section-specific references. 3. Return top issues ranked by severity and concrete LaTeX edits. 4. For JDS profile, explicitly call out line numbers, vector graphics, cleaned BibTeX, and reproducibility supplement readiness.
1. Use `references/50-review-report.md`. 2. Write summary + overall assessment + numbered major/minor comments. 3. Keep tone constructive and professional. 4. If requested, output using `assets/reviewer-report-template.tex`.
1. Use `references/51-response-to-reviewers.md`. 2. Structure by Editor, Associate Editor, Reviewer sections. 3. For every comment: quote, respond, quote manuscript chang
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