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Referee Response

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scdenney
referee-response

Organizes an author’s response to peer review. Extracts every referee and editor point, tags severity and type, orders the revision by dependency, flags defensible pushbacks as questions, and builds a numbered comment → response → location letter whose substantive answers the author writes. Use when the user has referee reports, a revise-and-resubmit decision, or an editor’s letter and wants to plan the revision, draft the response letter, or check that every point was addressed. The reviewer-side twin is journal-review.

Overview

Publisherscdenney
Repositoryopen-science-skills
Skill namereferee-response
Stars
69
Forks
3
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by scdenney on GitHub. Read the source before you install it.

Installation

Install the Referee Response AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/scdenney/open-science-skills/tree/main/plugin/skills/referee-response
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/scdenney/open-science-skills.git /tmp/open-science-skills
mkdir -p .claude/skills
cp -r /tmp/open-science-skills/plugin/skills/referee-response .claude/skills/referee-response
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Referee Response in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Referee Response on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Referee Response is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Referee Response

Turns scattered reviewer comments into an ordered revision plan and a response letter the author fills in. The author writes every substantive answer, including the interpretation of a result and the reason a request is declined; this skill makes sure each point is found, ordered, answered somewhere, and formatted the way editors expect.

Related skills. journal-review writes a referee report for someone else's manuscript; this skill answers one written about yours. sci-edit polishes the letter's prose after the author has written the answers. paper-review-lite and citation-check are useful before resubmission when a referee's point implies the manuscript needs a fresh audit.

Inputs

One or more referee reports, the editor's decision letter if available, and the manuscript (current draft, and the submitted version if the two differ). If a report is a PDF or Word file, read it with doc-to-markdown first. Ask once for anything missing that the steps below need; do not guess at a report's content.

Modes

Infer from the request; an argument forces it.

  • plan (default when no letter exists yet): steps 1–3.
  • letter: steps 1–4, producing the letter skeleton.
  • check: step 5 over a letter the author has filled in.

Step 1 — Extract every point

List every distinct request, criticism, or question across all reports and the editor's letter. One row per point, even when a referee bundles several into one paragraph. For each point record:

  • referee (R1, R2, editor), and the location in the report;
  • the point, quoted or closely paraphrased so the author can find it;
  • severity: blocking (the editor or a referee makes acceptance conditional on it), major, minor, optional (a suggestion the author may decline);
  • type: analysis, design, framing, writing, citation, data or code, presentation;
  • whether another referee raises the same point, agrees, or conflicts with it.

Count the points and tell the author the count per referee. A missed point is the commonest reason a resubmission fails, so the list is the deliverable of this step, not a summary of it.

Step 2 — Map dependencies and order the revision

Order the points so upstream changes come first: a new specification before the discussion that interprets it; a reframing before the sections that rest on it; a data correction before every number that depends on it. Group into a sequence (do first → depends on). Flag conflicts between referees as editor-judgment calls and say what each resolution would cost.

For each point in order: the change implied, the manuscript sections and files it touches, whether it needs new analysis, and a rough effort estimate. Where a point would distort the manuscript's argument if taken literally, say so.

Step 3 — Flag the pushbacks, as questions

Not every request should be accepted. Mark the points where a reasoned decline is defensible (the request contradicts the pre-registration, rests on a misreading, asks for an analysis the design cannot support, or would trade a confirmatory claim for an exploratory one) and give the reason. Put each to the author as a question; the decision is theirs. Do not write the decline itself.

Step 4 — Build the letter skeleton

The standard shape editors expect:

  1. Cover note to the editor. Thanks, a two-sentence summary of the main changes, and a pointer to the point-by-point response. Leave the summary as [AUTHOR: two sentences on the main changes].

  2. General response (optional): a short paragraph for changes that cut across several points. Placeholder.

  3. One section per referee, in the order the editor listed them. Within each, every point from step 1 in the referee's own order, as a numbered table or numbered blocks:

    #Reviewer commentResponseWhere in the manuscript
    R1.1quoted or paraphrased point[AUTHOR: …][section / page / line]

    Keep the reviewer's wording in the comment column so the referee recognizes the point. Pre-fill the response column only with the kind of response the plan implies ([AUTHOR: describe the new robustness table], [AUTHOR: reasoned decline, see step 3]), never with content. Where the same point appears under two referees, cross-reference rather than duplicate.

  4. Editor's points, if the letter contains any beyond the referees'.

Write the skeleton to response_to_reviewers.md beside the manuscript unless the author names another path. State how many placeholders it contains.

Step 5 — Check the filled letter

Once the author has written the responses:

  • Every point from step 1 has a non-placeholder response; list any that do not.
  • Every manuscript location cited in the letter exists in the current draft (section headings, table and figure numbers, page ranges); list any that do not resolve.
  • Every response that claims a change names where the change is; every reasoned decline gives a reason.
  • Tone: responses are courteous and specific, never defensive; flag sentences that argue with the referee rather than answer them, and leave the rewording to the author or to sci-edit.
  • Conflicting referees: the letter says which way the author went and why.

Report the checks as a short list of what passed and what needs the author's attention.

Notes

  • Heritage: grown from a personal referee-response skill (2026-05) into a library skill on 2026-09-02.

Frequently asked questions

What does the Referee Response AI skill do?

Organizes an author’s response to peer review. Extracts every referee and editor point, tags severity and type, orders the revision by dependency, flags defensible pushbacks as questions, and builds a numbered comment → response → location letter whose substantive answers the author writes. Use when the user has referee reports, a revise-and-resubmit decision, or an editor’s letter and wants to plan the revision, draft the response letter, or check that every point was addressed. The reviewer-side twin is journal-review.

Why use Referee Response on TypingMind?

Because you install it once and use it with any model. Referee Response is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Referee Response in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scdenney/open-science-skills/tree/main/plugin/skills/referee-response. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Referee Response?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Referee Response?

As many as you like. As long as a model supports skills, you can use Referee Response with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Referee Response AI skill free?

It is published on GitHub by scdenney. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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