Claude Scholar is a semi-automated research assistant for academic research and software development, supporting literature review, coding, experiments, reporting, writing, and project knowledge management. Computer science and AI researchers use it across the research workflow with several coding-agent platforms; the catalogue contains its skills, commands, agents, hooks, plugin, and instruction.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Galaxy-Dawn/claude-scholarWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/galaxy-dawn/claude-scholar/rebuttal)<a href="https://agentmods.dev/commands/galaxy-dawn/claude-scholar/rebuttal"><img src="https://agentmods.dev/badge/commands/galaxy-dawn/claude-scholar/rebuttal/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/galaxy-dawn/claude-scholar/rebuttal"><img src="https://agentmods.dev/badge/commands/galaxy-dawn/claude-scholar/rebuttal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.01210 |
| Opus 5 | $0.00007 | $0.00605 |
| Sonnet 5 | $0.00003 | $0.00242 |
| Haiku 4.5 | $0.00001 | $0.00121 |
Grade A, and why
rebuttal scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/rebuttal - 审稿响应工作流
启动系统化的rebuttal撰写流程，从审稿意见分析到最终rebuttal文档生成。
用法
/rebuttal [review_file]
参数:
review_file(可选): 包含审稿意见的文件路径- 如果不提供，将引导用户提供审稿意见
功能
此命令将启动完整的rebuttal撰写工作流：
- 获取审稿意见 - 读取或接收审稿意见
- 分析和分类 - 将意见拆成 atomic objections，并分类为Major/Minor/Typo/Misunderstanding
- 制定策略 - 为每条意见选择响应策略
- 撰写rebuttal - 生成结构化的回复文档
- 语气优化 - 确保专业、礼貌的表达
- 生成输出 - 保存最终的rebuttal文档
工作流程
步骤 1: 获取审稿意见
如果提供了review_file参数：
- 读取文件内容
- 识别审稿人数量和意见结构
如果未提供文件：
- 引导用户粘贴或描述审稿意见
- 确认审稿人数量
步骤 2: 分析和分类
优先使用review-response skill；如果当前 runtime 提供 rebuttal-writer agent，可以用它辅助分析：
- 按审稿人分组意见
- 拆分每条 atomic objection
- 分类为Major/Minor/Typo/Misunderstanding
- 识别优先级
步骤 3: 制定响应策略
为每条意见选择策略：
- Accept - 接受并改进
- Defend - 礼貌辩护
- Clarify - 澄清误解
- Experiment - 补充实验
步骤 4: 撰写Rebuttal
生成结构化的回复：
- 为每条意见撰写Response和Changes
- 包含具体的位置引用
- 提供证据和理由
- 每条 response 必须包含 evidence anchor：paper location、result table、figure、analysis artifact、citation、Evidence Record ID、planned experiment status，或
unresolved
步骤 5: 语气优化
检查和优化语气：
- 确保每个回复以感谢开始
- 避免防御性或攻击性表达
- 保持专业和尊重
步骤 6: 生成输出
保存最终文档：
rebuttal.md- 完整的rebuttal文档review-analysis.md- 审稿意见分析（可选）experiment-plan.md- 补充实验计划（如果需要补充实验）
输出文件
执行此命令后，将生成以下文件：
rebuttal.md
完整的rebuttal文档，包含：
- 开场白（感谢审稿人）
- 逐条回复（Response + Changes）
- 主要修改总结
review-analysis.md（可选）
审稿意见分析文档，包含：
- 意见分类统计
- 策略选择说明
- 需要补充的实验列表
experiment-plan.md（可选）
补充实验计划文档，包含：
- 需要补充的实验列表
- 每个实验的目的和预期结果
- 实验的优先级和时间估计
使用示例
示例 1: 提供审稿意见文件
/rebuttal reviews.txt
将读取reviews.txt文件中的审稿意见，并启动rebuttal撰写流程。
示例 2: 交互式输入
/rebuttal
将引导你粘贴或描述审稿意见，然后启动rebuttal撰写流程。
注意事项
重要原则
- 覆盖优先 - 可以使用人工阅读或脚本辅助统计 atomic objections；不要因为自然语言总结而漏掉具体意见
- 保持专业语气 - 所有回复都要礼貌、尊重、有理有据
- 提供具体证据 - 每个回复都要包含具体的位置引用和证据
- 完整性检查 - 确保所有审稿意见都得到回应
参考资源
此命令会自动使用以下参考文档：
review-classification.md- 意见分类标准response-strategies.md- 响应策略指南rebuttal-templates.md- 回复模板库tone-guidelines.md- 语气优化指南
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 158 lines · 14 tokens per session scan A 2c28f9ff4cd8
rebuttal is a command published in the GitHub repository Galaxy-Dawn/claude-scholar (5,431 stars, last pushed 16d ago), licensed MIT. It adds 14 tokens to every session and 1,210 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
rb-ask
A command for asking questions about the current codebase through RepoBrain, a code knowledge tool.
rb-refresh
A command that rebuilds RepoBrain's knowledge base for the current workspace. RepoBrain is a tool that keeps searchable project information for an agent.
pr-review
Generate a PR review report aggregating quality scan, coverage, complexity, and breaking changes.
task
Start TASK phase — task decomposition.
fix
User-triggered workflow to automatically fix open issues.
brainstorm
Pre-implementation collaborative requirement exploration and design (conversational brainstorm).