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.
npx skills add Galaxy-Dawn/claude-scholar --skill nature-responsegit 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/skills/galaxy-dawn/claude-scholar/nature-response)<a href="https://agentmods.dev/skills/galaxy-dawn/claude-scholar/nature-response"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/nature-response/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/skills/galaxy-dawn/claude-scholar/nature-response"><img src="https://agentmods.dev/badge/skills/galaxy-dawn/claude-scholar/nature-response.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00091 | $0.01329 |
| Opus 5 | $0.00046 | $0.00665 |
| Sonnet 5 | $0.00018 | $0.00266 |
| Haiku 4.5 | $0.00009 | $0.00133 |
Grade A, and why
nature-response 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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- nature-response — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Nature Reviewer Response Skill
Use this skill to convert editor decision letters, reviewer comments, author notes, or draft rebuttals into an auditable point-by-point response package for manuscript revisions.
The response letter is an editor-facing verification document. The goal is to show that every reviewer concern has been understood, addressed, and mapped to a concrete manuscript change, justified scientific response, or unresolved author action.
Default stance
- Preserve each reviewer comment faithfully before responding.
- Every reviewer concern must be answered, cross-referenced, or explicitly marked as unresolved.
- Map every response to manuscript evidence, a revision location, a justified disagreement, or
AUTHOR_INPUT_NEEDED. - Do not invent experiments, analyses, citations, line numbers, figure panels, supplementary materials, editor instructions, reviewer identities, or manuscript changes.
- Prefer concise, evidence-linked replies over long defensive explanations.
- When disagreeing, acknowledge the concern first, then give a scientific or scope-based reason.
- When a reviewer misunderstood the manuscript, first consider whether the manuscript presentation caused the misunderstanding.
- Treat rebuttal letters as potentially public review artifacts; write with professional tone and traceability.
Accepted inputs
The skill may receive:
- editor decision letter
- reviewer comments
- previous response draft
- manuscript change notes
- tracked-change summary
- line or page numbers
- figure, table, and supplement list
- author notes in Chinese or English
- journal name and article type
If reviewer boundaries or comment segmentation are ambiguous, flag the ambiguity instead of inventing reviewer structure.
Workflow
- Identify task mode and input readiness:
draft,audit,revise,triage-only, orappeal-like. - Identify decision type: minor revision, major revision, revise-and-resubmit, transfer after review, or unclear.
- Extract editor instructions first and assign IDs such as
E.1, then split reviewer comments with IDs such asR1.1,R1.2, andR2.1. - Classify each item by category, severity, action label, missing input, readiness state, and risk.
- Create a response strategy summary before drafting prose.
- Draft responses using preserved reviewer comments unless the mode is
triage-onlyorappeal-like. - Map each claimed change to manuscript location, figure, table, supplement, citation, or explicit placeholder.
- Flag missing author input rather than fabricating details.
- Run QA for completeness, traceability, factuality, tone, and unresolved risk.
- Return the response package with package readiness:
ready_to_submit,draft_with_placeholders,needs_author_input, orblocked.
What ships with it
20 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/conflicting-reviewers.md 1.4 KB
- examples/major-revision-with-missing-evidence.md 1.5 KB
- examples/minor-revision.md 1.7 KB
- README.md 4.1 KB
- references/action-mapping.md 3.6 KB
- references/chinese-author-alignment.md 3.3 KB
- references/comment-taxonomy.md 4.1 KB
- references/difficult-cases.md 4.0 KB
- references/intake-and-routing.md 4.9 KB
- references/qa-checklist.md 3.1 KB
- references/response-structure.md 3.5 KB
- references/source-basis.md 3.2 KB
- references/tone-and-stance.md 3.3 KB
- tests/conflicting-reviewers.md 1.8 KB
- tests/defensive-draft-audit.md 1.7 KB
- tests/evaluation-summary.md 2.2 KB
- tests/impossible-experiment.md 1.6 KB
- tests/major-revision-missing-evidence.md 1.8 KB
- tests/minor-revision.md 1.4 KB
- tests/rubric.md 2.6 KB
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.
- 7d ago First seen · 128 lines · 91 tokens per session scan A 9521d5cd6d66
nature-response is a skill published in the GitHub repository Galaxy-Dawn/claude-scholar (5,419 stars, last pushed 15d ago), licensed MIT. It adds 91 tokens to every session and 1,329 once invoked, about $0.0005 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 2026-09-03.
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stss
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aaai-experiments
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