Paper Revision
This handles the tedious part of paper revisions after peer review: mapping reviewer comments to sections, prioritizing major versus minor concerns, and tracking what needs to change where. It walks through parsing feedback, planning targeted edits, executing them with marked changes (using blue text highlights), and verifying you've actually addressed everything. The workflow includes hooks for running new experiments or generating figures if reviewers asked for additional results. Useful because it enforces a systematic approach instead of randomly jumping between comments, and it makes you document what changed and why. Keeps the previous version around and cross-references every edit to the original concern, which saves time when writing your response to reviewers.
- skills.sh installs
- 1.8K
- GitHub stars
- 459
- last updated
- Aug 21, 2026
Install
$ npx -y skills add lingzhi227/agent-research-skills --skill paper-revision --agent claude-codeInstalls into .claude/skills of the current project.
Files
Paper Revision
Systematically revise papers based on reviewer feedback.
Input
$0— Reviewer comments/feedback$1— Current paper draft (main.tex or paper directory)
References
- Revision workflow and prompts:
~/.claude/skills/paper-revision/references/revision-prompts.md
Workflow
Step 1: Parse and Prioritize Concerns
For each reviewer comment:
- Extract the specific concern
- Classify: major revision, minor revision, question, suggestion
- Map to affected paper section(s)
- Prioritize: address major concerns first
Step 2: Plan Revisions
Create a revision plan:
Concern → Affected Section → Required Action → New Content/Experiment
Categories of actions:
- Clarification: Rewrite text for clarity
- Additional experiment: Run new experiment, add results
- New analysis: Add ablation, statistical test, or comparison
- Structural change: Move, merge, or split sections
- Citation: Add missing references
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- Repository
- github.com/lingzhi227/agent-research-skills
- Categories
- Git & Pull Requests, Code Review & Quality, AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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