stage2-arr-refine 标准
Refine a Stage2 ARR (ACL Rolling Review) rebuttal draft into polished, reviewer-facing prose in the author's style; preserve factual grounding, optionally prepend a courteous opening phrase, and normalize tables/code/formulas into Markdown.
安装方式
cp -r skills/stage2/arr ~/.claude/skills/arr-2把该 skill 目录放进客户端 skills 目录。不同客户端的目录可能不同，Claude Code 默认是 ~/.claude/skills/
https://raw.githubusercontent.com/runtsang/RebuttalStudio/1f81e2f0aeccd582df2f5f84dfb3c17da5189599/skills/stage2/arr/SKILL.md下载后另存为 ~/.claude/skills/arr-2/SKILL.md。链接锁定采集时的 commit，内容与本站展示一致。
整仓一次性获取：git clone --depth 1 https://github.com/runtsang/RebuttalStudio.git
依赖与权限
- 无需 API Key
- 无需联网
- 无外部命令依赖
- 以上为按正文与 frontmatter 自动推断的信号，仅供参考；安装前建议先阅读原文。
摘要与原文
本站未内联该 skill 的正文（许可不在允许再分发的白名单内，或作者未声明许可）。下面是本站撰写的摘要，完整内容请查看原文。
Refine a Stage2 ARR (ACL Rolling Review) rebuttal draft into polished, reviewer-facing prose in the author's style; preserve factual grounding, optionally prepend a courteous opening phrase, and normalize tables/code/formulas into Markdown.
质量与来源
- 所属仓库
- runtsang/RebuttalStudio
- 仓库星标
- 218
- 最近更新
- 6 个月前
- 采集时间
- 今天
- commit
- 1f81e2f0aecc
- 内容指纹
- sha256:68f6019f1891f28
- 文件路径
- skills/stage2/arr/SKILL.md
同仓库其它 Skill
查看全部 21 个skills
Multi-stage rebuttal analysis skill for RebuttalStudio. Use when organizing reviewer comments into stage-specific conference workflows, including stage1 breakdown, stage2 refinement, stage4 multi-round follow-up, and stage5 final remarks generation.
document-memory-summarize
Summarize extracted paper text into concise Markdown memory for later Stage 2 and Stage 4 background use, with fixed section headings and no fabricated claims.
template-polish
Polish (rephrase) a rebuttal message template for clarity and professionalism while preserving the original structure, tone, and intent.
stage1-arr-breakdown
Break down full ARR (ACL Rolling Review) reviewer responses into structured rebuttal units. Use when input contains ARR reviewer fields (Paper Summary, Strengths, Weaknesses, Comments/Suggestions) and numeric scores (Confidence, Soundness, Excitement, Overall Assessment, Reproducibility). Splits weaknesses and comments/suggestions into granular response items while preserving original wording for quoted issues.
stage1-iclr-breakdown
Break down full ICLR reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted issues.
stage1-icml-breakdown
Break down full ICML reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted issues.
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planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; Gemini lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. The session-end hook reports status only; it does not request continuation or run commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; lifecycle hooks inject selected project planning context. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. Optional gated mode can request continuation only when the host supports it and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
planning-with-files
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; Kiro skill instructions and steering state read selected project planning context. Recovery reads project planning files and their timestamps only, not agent transcript stores. This adapter registers no Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.