skills/42-wanshuiyin-ARIS/skills/skills-codex/rebuttal/SKILL.md
Workflow 4: Submission rebuttal pipeline. Parses external reviews, enforces coverage and grounding, drafts a safe text-only rebuttal under venue limits, and manages follow-up rounds.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research rebuttalInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Prepare and maintain a grounded, venue-compliant rebuttal for: $ARGUMENTS
This skill is optimized for:
This skill does not:
AUTO_EXPERIMENT = trueWorkflow 1: idea-discovery
Workflow 1.5: experiment-bridge
Workflow 2: auto-review-loop (pre-submission)
Workflow 3: paper-writing
Workflow 4: rebuttal (post-submission external reviews)
ICML — Default venueTEXT_ONLY — v1 defaultgpt-5.4 — Used via a secondary Codex agent for internal stress-testingtrue, invoke /experiment-bridge for reviewer concerns that require new evidencetrue, only run Phase 0-3 and stop after strategyrebuttal/Override:
/rebuttal "paper/" — venue: NeurIPS, character limit: 5000
If venue rules or limit are missing, stop and ask before drafting.
Three hard gates. If any fails, do not finalize:
rebuttal/REBUTTAL_STATE.md exists, resume from the recorded phaserebuttal/ and initialize the output documentsrebuttal/REVIEWS_RAW.md verbatimrebuttal/REBUTTAL_STATE.mdCreate rebuttal/ISSUE_BOARD.md.
For each atomic concern, record:
issue_idreviewer, round, raw_anchorissue_typeseverityreviewer_stanceresponse_modestatusCreate rebuttal/STRATEGY_PLAN.md.
QUICK_MODE exit: if QUICK_MODE = true, stop here and present ISSUE_BOARD.md + STRATEGY_PLAN.md.
Skip entirely if AUTO_EXPERIMENT is false.
If the strategy plan identifies issues that require new empirical evidence:
/experiment-bridge "rebuttal/REBUTTAL_EXPERIMENT_PLAN.md"ISSUE_BOARD.mdnarrow_concession or future_work_boundaryrebuttal/REBUTTAL_EXPERIMENTS.mdCreate rebuttal/REBUTTAL_DRAFT_v1.md.
Structure:
Also generate rebuttal/PASTE_READY.txt with exact character count.
Run all lints:
spawn_agent:
model: gpt-5.4
reasoning_effort: xhigh
message: |
Stress-test this rebuttal draft:
[raw reviews + issue board + draft + venue rules]
1. Unanswered or weakly answered concerns?
2. Unsupported factual statements?
3. Risky or unapproved promises?
4. Tone problems?
5. Paragraph most likely to backfire with a meta-reviewer?
6. Minimal grounded fixes only. Do not invent evidence.
Verdict: safe to submit / needs revision
Save the full response to rebuttal/MCP_STRESS_TEST.md. If a hard safety blocker remains, revise before finalizing.
Produce:
rebuttal/PASTE_READY.txt — strict version, ready to pasterebuttal/REBUTTAL_DRAFT_rich.md — extended version with optional sections markedrebuttal/REBUTTAL_STATE.mdWhen new reviewer comments arrive:
rebuttal/FOLLOWUP_LOG.mdsend_inputtools
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development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
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