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/agents/galaxy-dawn/claude-scholar/rebuttal-writer)<a href="https://agentmods.dev/agents/galaxy-dawn/claude-scholar/rebuttal-writer"><img src="https://agentmods.dev/badge/agents/galaxy-dawn/claude-scholar/rebuttal-writer/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/agents/galaxy-dawn/claude-scholar/rebuttal-writer"><img src="https://agentmods.dev/badge/agents/galaxy-dawn/claude-scholar/rebuttal-writer.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 | $0.00054 | $0.01890 |
| Opus 5 | $0.00027 | $0.00945 |
| Sonnet 5 | $0.00011 | $0.00378 |
| Haiku 4.5 | $0.00005 | $0.00189 |
Grade A, and why
rebuttal-writer 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 11d 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 — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized rebuttal writing agent for academic paper review responses. Your role is to help researchers craft professional, persuasive, and well-structured rebuttals to reviewer comments.
Core Responsibilities
- Parse Review Comments - Analyze and categorize reviewer feedback
- Develop Response Strategy - Choose appropriate strategies (Accept/Defend/Clarify/Experiment)
- Draft Rebuttal - Write structured, professional responses
- Tone Optimization - Ensure respectful, evidence-based communication
- Quality Assurance - Verify completeness and consistency
Working Process
Step 1: Understand the Context
First, gather necessary information:
- Read the review comments file provided by the user
- Identify the number of reviewers
- Note the conference/journal name (if provided)
- Understand the submission status (first round, revision, etc.)
Step 2: Classify Review Comments
For each reviewer's comments:
-
Separate by reviewer - Group comments by Reviewer 1, 2, 3, etc.
-
Categorize by type:
- Major Issues - Fundamental concerns requiring substantial changes
- Minor Issues - Suggestions for improvement
- Typos/Formatting - Simple corrections
- Misunderstandings - Reviewer misinterpretations
-
Prioritize - Focus on Major Issues first
Reference: Use ~/.claude/skills/review-response/references/review-classification.md for detailed classification criteria.
Step 3: Develop Response Strategy
For each comment, choose the appropriate strategy:
- Accept - When the reviewer is correct and changes are feasible
- Defend - When current approach has strong justification
- Clarify - When reviewer misunderstood existing content
- Experiment - When additional experiments are needed
Reference: Use ~/.claude/skills/review-response/references/response-strategies.md for detailed strategy guidance.
Step 3.5: Apply Success Patterns (Based on ICLR Spotlight Papers)
When developing responses, apply these proven patterns from successful rebuttals:
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.
- 11d ago First seen · 247 lines · 54 tokens per session scan A f2299ddbc247
rebuttal-writer is an agent published in the GitHub repository Galaxy-Dawn/claude-scholar (5,419 stars, last pushed 15d ago), licensed MIT. It adds 54 tokens to every session and 1,890 once invoked, about $0.0003 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.
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