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QinghongLin/paperdoctor

PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress

¿Qué es paperdoctor?

paperdoctor is a Claude Code agent skill that paperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add QinghongLin/paperdoctor

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Documentación

Run Experiments

This skill requires explicit user approval before execution. Do not start automatically after Phase 2 — present the reproduction plan summary and wait for the user to confirm.

Prerequisites

  • {paper_dir}/reports/check_exp.json — experiment plan from read-exp (priority, feasibility, commands)
  • {paper_dir}/reports/check_code.json — code issues from read-code (fix before running)

Workflow

- [ ] Step 1: Create environment
- [ ] Step 2: Run experiments

Step 1: Create Environment

conda create -n paperdr-{paper_stem} python=<version> -y
conda activate paperdr-{paper_stem}
# follow README for install

Download data/checkpoints as needed. Fix any warning/error items from check_code.json before running.


Step 2: Run Experiments

Run experiments from check_exp.json in priority order (high first). Prefer eval over training.

For each experiment, compare output against the paper's numbers. Update check_exp.json with:

{
  "our_result": {"metric": 0.0},
  "status": "pass",
  "note": "..."
}
StatusMeaning
passMatches paper within ~1-2%
warningPartial match or needed fixes
errorSignificant difference or could not run

Tips

  • Follow the repo's README — do not guess
  • OOM is error — note cause, do not retry endlessly
  • Record exact command and hardware used

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