R&R Rebuttal (car-rebuttal)

SkillCommerce & finance

Use when a Contemporary Accounting Research (CAR) revise-and-resubmit arrives, planning the revision and drafting a point-by-point response to two reviewers and the subject Editor, including any new analyses, robustness, and updated Data Integrity/code-sharing materials. Drafts the response; it does not run the new estimation (car-data-analysis) or the final preflight (car-submission).

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the R&R Rebuttal (car-rebuttal) skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Contemporary-Accounting-Research-Skills/skills/car-rebuttal/SKILL.md and read by ahel’s review.

When to trigger

  • You received a CAR revise-and-resubmit and must plan revisions and the response
  • Reviewers asked for new identification tests, experiments, or model robustness
  • The subject Editor's letter sets priorities you must address first
  • You need to reconcile the response with CAR's code-sharing and data-availability requirements

Plan the revision before writing the letter

  • Build a concordance: every point from Reviewer 1, Reviewer 2, and the subject Editor, each mapped to a concrete action (new test, added robustness, reframing, clarification, or a reasoned push-back).
  • Let the subject Editor's letter set priority — it signals which concerns are decisive at CAR, where the subject Editor disposes and the EIC approves all acceptances.
  • Do the analytical/empirical work first; only then draft the response. Invited revisions pay no further submission fee, so the constraint is substance and the editor's deadline, not cost.

Respond by tradition

  • Archival. Reviewers typically press on identification and robustness — new natural-experiment evidence, parallel-trends/placebo tests, alternative measures, additional fixed effects, or sample-screen sensitivity. Show, don't assert, that the inference survives.
  • Experimental. Expect requests on confounds, the mediator, manipulation strength, and the participant pool — possibly a new condition or a follow-up study isolating the mechanism.
  • Analytical. Expect requests to relax assumptions, add comparative statics, or sharpen the empirical/institutional implication.

Write the point-by-point response

  • Quote each comment, then give the response and the exact manuscript location of the change (page/section/table).
  • Be specific and courteous; where you disagree, argue from theory or evidence, not assertion.
  • Keep a verifiable change-trail; ensure new results are reproducible from the code repository.

Update CAR-specific materials

  • Refresh the data availability statement and the public code repository if analyses changed, keeping variable definitions, omission rules, and modifications (winsorizing/truncating) documented; honor the six-year retention assurance.
  • Disclose any new generative-AI use in Methods; if a related paper of yours has since appeared, update the overlap disclosure.
  • Re-check the length budget (30/50 pages); route new robustness to the online-only appendix.

Reproducibility change ledger

Every new or changed result needs a reproducibility row:

Revision itemTable / figureCode or data changeDisclosure update
New robustness or alternative measureMain or online appendix tableScript name, variable definition, sample restrictionData availability / repository note
New experiment or archival sampleStudy/table numberInstrument, randomization, or sample construction fileEthics, data, and retention note
Revised construct or proxyHypothesis/table locationRenamed variable and transformation logConstruct/proxy explanation
AI-assisted text/code cleanupMethods or disclosure noteTool use and human verificationAI disclosure if required

Do not resubmit until a fresh clone of the repository or analysis folder regenerates all changed exhibits. CAR's data-integrity expectations make stale replication files a substantive defect, not an administrative detail.

Checklist

  • Concordance covers every reviewer and editor point with a concrete action
  • Subject Editor's priorities addressed first
  • New analyses/experiments/robustness completed and reproducible from the repo
  • Point-by-point response cites exact change locations; disagreements argued, not asserted
  • Data availability statement and code repository updated; retention assurance intact
  • AI disclosure and own-work overlap disclosure refreshed
  • Length budget respected; overflow in the online appendix
  • Reproducibility change ledger completed and regenerated from a clean run

Anti-patterns

  • Drafting the letter before doing the work — CAR reviewers verify the manuscript actually changed.
  • Selective response that quietly skips a reviewer's hard point.
  • Defensive push-back without theory or evidence.
  • Stale reproducibility — updated tables the archived code no longer regenerates.
  • Disclosure lag — new analyses added but data availability, AI, or overlap statements left in the old state.

Output format

【Concordance】R1 / R2 / subject Editor points → actions ...
【Priority】editor's first-order items addressed first?
【New work】tests/experiments/robustness done & reproducible?
【Response letter】each comment quoted, answered, located?
【CAR materials】data availability statement, code repo, AI & overlap disclosures updated?
【Reproducibility ledger】changed exhibits regenerate from clean run?
【Next step】car-submission (final preflight) → resubmit in Editorial Manager

Signals

GitHub stars
1k
Forks
155
Last commit
Sep 2026
Advanced
Item type
skill
Key
car-rebuttal
Source
github.com/brycewang-stanford/awesome-journal-skills