pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Draft academic paper sections with notation protocol, anti-hedging, and humanizer pass. Replaces /draft-paper and /humanizer.
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill write --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill write -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill write -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill write -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill write -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill write -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill write -a windsurf -y
name: write description: Draft academic paper sections with notation protocol, anti-hedging, and humanizer pass. Replaces /draft-paper and /humanizer. argument-hint: "[section or mode: intro | strategy | results | conclusion | abstract | full | humanize] [file path (optional)]" allowed-tools: Read,Grep,Glob,Write,Edit,Task
Draft paper sections or apply humanizer pass by dispatching the **Writer** agent.
**Input:** `$ARGUMENTS` — section name or mode, optionally followed by file path.
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Draft a specific section: `intro`, `strategy`, `results`, `conclusion`, `abstract`, or `full`.
**Agent:** Writer **Output:** LaTeX section file in paper/sections/
Workflow:
Before drafting, read all available context: 1. Read existing paper draft in `paper/` (if it exists) 2. Read `master_supporting_docs/` for notes, outlines, research specs 3. Read most recent `quality_reports/research_spec_*.md` or `quality_reports/lit_review_*.md` 4. Read `.claude/references/domain-profile.md` for field conventions 5. Check `Bibliography_base.bib` for available citations 6. Scan `paper/tables/` and `paper/figures/` for generated output 7. Read `quality_reports/results_summary.md` if it exists (from Coder)
Based on `$ARGUMENTS`:
Dispatch Writer with section standards, notation protocol, and anti-hedging rules. Humanizer pass runs automatically before finalizing. Save to `paper/sections/[section].tex`.
Before presenting the draft:
Present each section for feedback. Flag items that need attention:
Strip AI writing patterns from existing text without rewriting content.
**Agent:** Writer (humanizer mode) **Output:** Edited file with AI patterns removed
Strips 24 patterns across 4 categories:
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| Section | Length | Key Requirements | |---------|--------|-----------------| | Introduction | 1000-1500 words | Hook → question → method → finding → contribution → roadmap | | Strategy | 800-1200 words | Formal assumption, numbered equation, threats addressed | | Results | 800-1500 words | Main spec, effect sizes in economic terms, heterogeneity | | Conclusion | 500-700 words | Restate with effect size, policy, limitations, future | | Abstract | 100-150 words | Question, method, finding with magnitude, implication |
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