R&R Rebuttal (car-rebuttal)
SkillCommerce & financeUse 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.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
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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 item | Table / figure | Code or data change | Disclosure update |
|---|---|---|---|
| New robustness or alternative measure | Main or online appendix table | Script name, variable definition, sample restriction | Data availability / repository note |
| New experiment or archival sample | Study/table number | Instrument, randomization, or sample construction file | Ethics, data, and retention note |
| Revised construct or proxy | Hypothesis/table location | Renamed variable and transformation log | Construct/proxy explanation |
| AI-assisted text/code cleanup | Methods or disclosure note | Tool use and human verification | AI 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
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- skill
- Key
car-rebuttal- Source
- github.com/brycewang-stanford/awesome-journal-skills
github.com/brycewang-stanford/awesome-journal-skills
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