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Research
Skill

/paper-revision

Revise papers based on reviewer feedback. Map reviewer concerns to specific sections, apply targeted edits, run additional experiments if needed, and verify improvements. Use after receiving peer review with revision requests.

From plugin
agent-research-skills
26531 skills1 command
Install
$ npx -y skills add lingzhi227/agent-research-skills --skill paper-revision --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/paper-revision

Context preview

The summary Claude sees to decide when to auto-load this skill.

Revise papers based on reviewer feedback. Map reviewer concerns to specific sections, apply targeted edits, run additional experiments if needed, and verify improvements. Use after receiving peer review with revision requests.

SKILL.md

paper-revision.SKILL.md
name: paper-revision
description: Revise papers based on reviewer feedback. Map reviewer concerns to specific sections, apply targeted edits, run additional experiments if needed, and verify improvements. Use after receiving peer review with revision requests.
argument-hint: [reviews-or-draft]

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: 1. Extract the specific concern 2. Classify: major revision, minor revision, question, suggestion 3. Map to affected paper section(s) 4. 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

Step 3: Execute Revisions

For each planned revision: 1. Read the current section 2. Apply targeted edits (preserve surrounding structure) 3. If new experiments needed: use experiment-code skill 4. If new figures/tables needed: use figure-generation / table-generation skills 5. Mark changes (use `\textcolor{blue}{...}` for revised text)

Step 4: Verify Improvements

  • Re-run self-review skill to check if scores improved
  • Verify all reviewer concerns are addressed
  • Check that revisions don't introduce new issues
  • Ensure page count still fits venue requirements

Step 5: Write Revision Summary

Generate a diff summary:

  • List all changes made with section references
  • Note any new experiments, figures, or tables added
  • Cross-reference each change to the reviewer concern it addresses

Rules

  • Address EVERY reviewer concern — do not skip any
  • Preserve paper structure unless structural change is explicitly needed
  • New results must come from actual experiments, not hallucinated
  • Mark all revised text clearly for the reviewers
  • Keep a copy of the previous version before revising
  • Compare new scores vs previous scores after revision

Related Skills

  • Upstream: [self-review](../self-review/)
  • Downstream: [paper-compilation](../paper-compilation/)
  • See also: [rebuttal-writing](../rebuttal-writing/), [paper-writing-section](../paper-writing-section/)
Read more
Ships withagent-research-skills

31 skills for Claude Code covering the full academic research paper lifecycle — from literature search to slide generation — plus GitHub repository analysis for research topics. Extracted from 17 GitHub repos studying LLM-agent-driven research automation.

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Repo: lingzhi227/agent-research-skills

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