pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Utility commands — commit, compile, validate-bib, journal, context-status, deploy, learn. Replaces individual utility skills.
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill tools --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/toolsContext preview
The summary Claude sees to decide when to auto-load this skill.
Utility commands — commit, compile, validate-bib, journal, context-status, deploy, learn. Replaces individual utility skills.
name: tools description: Utility commands — commit, compile, validate-bib, journal, context-status, deploy, learn. Replaces individual utility skills. argument-hint: "[subcommand: commit | compile | validate-bib | journal | context | deploy | learn | upgrade] [args]" allowed-tools: Read,Grep,Glob,Write,Edit,Bash,Task
Utility subcommands for project maintenance and infrastructure.
**Input:** `$ARGUMENTS` — subcommand followed by any arguments.
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Stage changes, create commit, optionally create PR and merge.
3-pass XeLaTeX + bibtex compilation.
For papers:
cd Paper && TEXINPUTS=../Preambles:$TEXINPUTS xelatex -interaction=nonstopmode [file] BIBINPUTS=..:$BIBINPUTS bibtex [file_base] TEXINPUTS=../Preambles:$TEXINPUTS xelatex -interaction=nonstopmode [file] TEXINPUTS=../Preambles:$TEXINPUTS xelatex -interaction=nonstopmode [file]
For talks:
cd Talks && TEXINPUTS=../Preambles:$TEXINPUTS xelatex -interaction=nonstopmode [file]
Cross-reference all \cite{} keys in paper and talk files against Bibliography_base.bib. Report: missing entries, unused entries, duplicate keys.
Regenerate the research journal timeline from quality reports and git history. Shows chronological record of agent actions, phase transitions, scores, decisions.
Show current context status and session health. Check context usage, whether auto-compact is approaching, what state will be preserved.
Render Quarto guide site and publish to GitHub Pages.
cd guide && quarto publish gh-pages --no-browser
Extract reusable knowledge from the current session. Auto-memory handles corrections automatically; this is for multi-step workflows worth turning into a full skill.
Upgrade an existing project to the latest clo-author architecture.
**What it does:** 1. Clone the latest clo-author release into a temp directory 2. Save the user's filled-in domain-profile.md and any custom journal profiles 3. Delete the old `.claude/` directory 4. Copy the new `.claude/` in 5. Restore the user's domain-profile.md and custom journal profiles 6. Optionally copy new `templates/` 7. Report what changed
**Workflow:**
Step 1: DOWNLOAD - Clone latest clo-author into /tmp/clo-author-upgrade - Or: gh release download --repo hugosantanna/clo-author Step 2: PRESERVE USER CUSTOMIZATIONS - Save .claude/references/domain-profile.md if filled in (not just placeholders) - Save any custom journal profiles the user added to journal-profiles.md - Save .claude/settings.json (user's permissions and hooks) - Save .claude/settings.local.json if it exists Step 3: REPLACE - Delete old .claude/ entirely - Copy new .claude/ from the downloaded release - Restore saved customizations from Step 2 Step 4: DO NOT TOUCH - paper/, scripts/, data/, explorations/, quality_reports/ - CLAUDE.md, Bibliography_base.bib, README.md, .gitignore - Any other user content Step 5: REPORT - List what was updated (new agents, skills, rules) - List what was preserved (domain profile, settings, custom profiles) - Clean up temp directory
**No git merge. No upstream remote. No conflicts.** Just delete and replace `.claude/`.
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📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud /…
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation +…
Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest +…
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want…