pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Multi-agent review for research presentation slides in the sewage-house-prices project (visual, econometric fidelity, proofreading, substance). Use for comprehensive quality check before milestones.
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill slide-excellence --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill slide-excellence -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill slide-excellence -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill slide-excellence -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill slide-excellence -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill slide-excellence -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill slide-excellence -a windsurf -y
name: slide-excellence description: Multi-agent review for research presentation slides in the sewage-house-prices project (visual, econometric fidelity, proofreading, substance). Use for comprehensive quality check before milestones. argument-hint: "[TEX filename]" allowed-tools: ["Read", "Grep", "Glob", "Write", "Task"] context: fork
Run a comprehensive multi-dimensional review of research presentation slides. Multiple agents analyze the file independently, then results are synthesized.
Parse `$ARGUMENTS` for the filename. Resolve path in `docs/conferences/`.
**Agent 1: Visual Audit** (slide-auditor)
**Agent 2: Econometric Fidelity Review** (econometrics-reviewer)
**Agent 3: Proofreading** (proofreader)
**Agent 4: TikZ Review** (only if file contains TikZ)
**Agent 5: Substance Review** (domain correctness)
# Slide Excellence Review: [Filename] ## Overall Quality Score: [EXCELLENT / GOOD / NEEDS WORK / POOR] | Dimension | Critical | Medium | Low | |-----------|----------|--------|-----| | Visual/Layout | | | | | Econometric Fidelity | | | | | Proofreading | | | | | Substance | | | | ### Critical Issues (Immediate Action Required) ### Medium Issues (Next Revision) ### Recommended Next Steps
| Score | Critical | Medium | Meaning | |-------|----------|--------|---------| | Excellent | 0-2 | 0-5 | Ready to present | | Good | 3-5 | 6-15 | Minor refinements | | Needs Work | 6-10 | 16-30 | Significant revision | | Poor | 11+ | 31+ | Major restructuring |
## 🔥 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 +…
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