pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Comprehensive manuscript review covering argument structure, econometric specification, citation completeness, and potential referee objections. Make sure to use this skill whenever the user wants substantive academic feedback on a paper — not just surface edits. Triggers
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-paper --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-paper -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-paper -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-paper -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-paper -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-paper -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-paper -a windsurf -y
name: review-paper description: >- Comprehensive manuscript review covering argument structure, econometric specification, citation completeness, and potential referee objections. Make sure to use this skill whenever the user wants substantive academic feedback on a paper — not just surface edits. Triggers include: "review my paper", "give me feedback on this draft", "what would a referee say", "anticipate referee objections", "act as a referee", "check my identification strategy", "is my argument convincing", "review this manuscript", "critique my paper", "will this pass review", or any request for deep critique of academic writing beyond typos and grammar. argument-hint: "[paper filename in manuscripts/ or path to .tex/.pdf]" allowed-tools: ["Read", "Grep", "Glob", "Write", "Task"]
Produce a thorough, constructive review of an academic manuscript — the kind of report a top-journal referee would write.
**Input:** `$ARGUMENTS` — path to a paper (.tex, .pdf, or .qmd), or a filename in `manuscripts/` or `references/papers/`.
---
1. **Locate and read the manuscript.** Check:
2. **Read the full paper** end-to-end. For long PDFs, read in chunks (5 pages at a time).
3. **Dispatch `domain-reviewer` agent** via Task for deep substance review (see below).
4. **Evaluate writing quality and presentation** (dimensions 5-6) — the skill handles these directly since the agent explicitly does not cover presentation.
5. **After the agent completes**, merge its findings with your writing/presentation evaluation. Generate 3-5 "referee objections" synthesized from both.
6. **Produce the unified review report.**
7. **Save to** `quality_reports/paper_review_[sanitized_name].md`
---
Dispatch the `domain-reviewer` agent via Task for the deep substance check. The agent applies 5 lenses that go deeper than broad dimensional evaluation — actual equation verification, derivation step checking, code-theory alignment, and backward logic tracing.
Task prompt: "You are the domain-reviewer agent. Review the manuscript at [path]. Research question: [from spec if available]. Apply all 5 review lenses: 1. Assumption stress test 2. Derivation verification 3. Citation fidelity 4. Code-theory alignment 5. Backward logic check Also check cross-document consistency. Follow the domain-reviewer agent instructions and return your full substance review report."
After the agent completes, collect its findings. These feed into the "Major Concerns" and "Referee Objections" sections of the final report.
---
The skill evaluates dimensions 5-6 directly (the agent does not cover these), then merges everything into the unified report format below.
---
---
# Manuscript Review: [Paper Title] **Date:** [YYYY-MM-DD] **Reviewer:** review-paper skill **File:** [path to manuscript] ## Summary Assessment **Overall recommendation:** [Strong Accept / Accept / Revise & Resubmit / Reject] [2-3 paragraph summary: main contribution, strengths, and key concerns] ## Strengths 1. [Strength 1] 2. [Strength 2] 3. [Strength 3] ## Major Concerns ### MC1: [Title] - **Dimension:** [Identification / Econometrics / Argument / Literature / Writing / Presentation] - **Issue:** [Specific description] - **Suggestion:** [How to address it] - **Location:** [Section/page/table if applicable] [Repeat for each major concern] ## Minor Concerns ### mc1: [Title] - **Issue:** [Description] - **Suggestion:** [Fix] [Repeat] ## Referee Objections These are the tough questions a top referee would likely raise: ### RO1: [Question] **Why it matters:** [Why this could be fatal] **How to address it:** [Suggested response or additional analysis] [Repeat for 3-5 objections] ## Specific Comments [Line-by-line or section-by-section comments, if any] ## Summary Statistics | Dimension | Rating (1-5) | |-----------|-------------| | Argument Structure | [N] | | Identification | [N] | | Econometrics | [N] | | Literature | [N] | | Writing | [N] | | Presentation | [N] | | **Overall** | **[N]** |
---
## 🔥 力荐 · StatsPAI — Stata 与 R 的 Python 平替,为 AI Agent 而生 🔁 Stata / R 平替:reghdfe、csdid、rdrobust、synth、fixest、did 等常用命令在 Python 中一站式替代,一个 import statspai as sp 搞定 DID / RD / IV / SCM / DML / matching 🤖 面向 Agent 设计:结构化结果对象 + 内置 MCP Server,Claude / Codex
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics…
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid +…
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven +…
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready…
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…