paper-referee-revise
Revise an academic paper based on journal referee reports. Reads referee comments from review report or annotated manuscript, then directly modifies main.tex one comment at a time with user approval. Generates response letter after revision. Trigger when user says "referee revise" / "paper-referee-revise" / "审稿意见修改" / "根据审稿人意见修改" / "referee report".
npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill paper-referee-revise --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Revises an academic paper based on journal referee reports. Reads referee comments from a review file or annotated manuscript, then modifies the manuscript one comment at a time with user approval. Generates a response letter after revision. Trigger when the user mentions the keywords for referee revision.
How it works
- Accept a project folder containing main.tex, manuscript subfolders, and referee materials (reports or annotated files).
- Identify the correct source for the current round: main.tex for round 1, or main-r{X-1}.tex for round X.
- Scan for referee files (referee*, report*, review*, R1*, annotated*, comments*, feedback*, revision*).
- Read and categorize each referee comment as Empirical, Text, or Editorial.
- Structure each comment as a discrete revision item with an ID (e.g., R1-1), type, location, and original comment.
- Present comments grouped by referee to the user and wait for confirmation to proceed.
- If empirical comments exist, generate empirical-revise.tex outlining required empirical tasks and halt with a printed status message.
- If no empirical comments exist, proceed to Phase 4: for each comment, display the exact text, show the matching original text in main-rX.tex, propose a revision wrapped in \textcolor{red}{...}, and ask the user to approve, modify, skip, or provide their own revision.
- Maintain a revision log including whether a comment was applied or skipped and the final revised text.
- After all comments, generate response-rX.tex as a detailed, self-contained response letter describing changes and referencing revised passages.
When to use it
Trigger phrases include: "referee revise" / "paper-referee-revise" / "审稿意见修改" / "根据审稿人意见修改" / "referee report".
What it can touch
The system edits main-rX.tex (and uses color markup via \textcolor{red}{...} for changes). It references table/figure numbers and may require auxiliary files for new content.
Caveats
No empirical revisions proceed unless empirical-revise.tex is generated and the user completes empirical work first. All changes are applied only after explicit user approval per comment. The main manuscript (main.tex) is never overwritten directly; round edits target main-rX.tex. The summary notes emphasize that the output includes a detailed response letter (response-rX.tex).
# Paper Referee Revise ## Overview This skill revises an academic paper (`main.tex`) based on **journal referee reports** received during the peer review process. The user provides a project folder containing the manuscript, supporting files, and referee materials. The skill extracts referee comments, triages empirical work, then processes text revisions **one by one** with user approval, and fina
What does the paper-referee-revise skill do?
Revise an academic paper based on journal referee reports. Reads referee comments from review report or annotated manuscript, then directly modifies main.tex one comment at a time with user approval. Generates response letter after revision. Trigger when user says "referee revise" / "paper-referee-revise" / "审稿意见修改" / "根据审稿人意见修改" / "referee report".
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill paper-referee-revise --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.