skills/42-wanshuiyin-ARIS/skills/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. Use when user says "rebuttal", "reply to reviewers", "ICML rebuttal", "OpenReview response", or wants to answer external reviews safely.
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:
If the user already has new results, derivations, or approved commitments, the skill can incorporate them as user-confirmed evidence.
Workflow 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 venue. Override if needed.TEXT_ONLY — v1 default.gpt-5.4 — Used via Codex MCP for internal stress-testing.true, automatically invoke /experiment-bridge to run supplementary experiments when the strategy plan identifies reviewer concerns that require new empirical evidence. When false (default), pause and present the evidence gap to the user for manual handling.true, only run Phase 0-3 (parse reviews, atomize concerns, build strategy). Outputs ISSUE_BOARD.md + STRATEGY_PLAN.md and stops — no drafting, no stress test. Useful for quickly understanding what reviewers want before deciding how to respond.rebuttal/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:
paper, review, user_confirmed_result, user_confirmed_derivation, or future_work. No source = blocked.already_done, approved_for_rebuttal, or future_work_only. Not approved = blocked.answered, deferred_intentionally, or needs_user_input. No issue disappears.rebuttal/REBUTTAL_STATE.md exists → resume from recorded phaserebuttal/, initialize all output documentsrebuttal/REVIEWS_RAW.md (verbatim)rebuttal/REBUTTAL_STATE.mdCreate rebuttal/ISSUE_BOARD.md.
For each atomic concern:
issue_id (e.g., R1-C2)reviewer, round, raw_anchor (short quote)issue_type: assumptions / theorem_rigor / novelty / empirical_support / baseline_comparison / complexity / practical_significance / clarity / reproducibility / otherseverity: critical / major / minorreviewer_stance: positive / swing / negative / unknownresponse_mode: direct_clarification / grounded_evidence / nearest_work_delta / assumption_hierarchy / narrow_concession / future_work_boundarystatus: open / answered / deferred / needs_user_inputCreate rebuttal/STRATEGY_PLAN.md.
QUICK_MODE exit: If QUICK_MODE = true, stop here. Present ISSUE_BOARD.md + STRATEGY_PLAN.md to the user and summarize: how many issues per reviewer, shared vs unique concerns, recommended priorities, and evidence gaps. The user can then decide to continue with full rebuttal (/rebuttal — quick mode: false) or write manually.
Skip entirely if AUTO_EXPERIMENT is false — instead, pause and present the evidence gaps to the user.
If the strategy plan identifies issues that require new empirical evidence (tagged response_mode: grounded_evidence with evidence_source: needs_experiment):
Generate a mini experiment plan from the reviewer concerns:
Invoke /experiment-bridge with the mini plan:
/experiment-bridge "rebuttal/REBUTTAL_EXPERIMENT_PLAN.md"
Wait for results, then update ISSUE_BOARD.md:
user_confirmed_resultIf experiments fail or are inconclusive:
narrow_concession or future_work_boundarySave experiment results to rebuttal/REBUTTAL_EXPERIMENTS.md for provenance tracking.
Time guard: If estimated GPU-hours exceed rebuttal deadline, skip and flag for manual handling.
Create rebuttal/REBUTTAL_DRAFT_v1.md.
Structure:
Default reply pattern per issue:
Heuristics from 5 successful rebuttals:
Hard rules:
Also generate rebuttal/PASTE_READY.txt (plain text, exact character count).
Run all lints:
mcp__codex__codex:
config: {"model_reasoning_effort": "xhigh"}
prompt: |
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 meta-reviewer?
6. Minimal grounded fixes only. Do NOT invent evidence.
Verdict: safe to submit / needs revision
Save full response to rebuttal/MCP_STRESS_TEST.md. If hard safety blocker → revise before finalizing.
Produce two outputs for different purposes:
rebuttal/PASTE_READY.txt — the strict version
rebuttal/REBUTTAL_DRAFT_rich.md — the extended version
[OPTIONAL — cut if over limit] for sections that exceed the strict versionUpdate rebuttal/REBUTTAL_STATE.md
Present to user:
PASTE_READY.txt character count vs venue limitREBUTTAL_DRAFT_rich.md for review and manual editingWhen new reviewer comments arrive:
rebuttal/FOLLOWUP_LOG.mdtools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
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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