Agent skill

Review Response

by flonat in flonat/flonat-research

Systematic reviewer response workflow: parse comments, classify by severity, develop response strategy, write structured rebuttal.

MITAuto-check passedResearch & Science

Install Review Response

skills CLI
$ npx skills add flonat/flonat-research --skill review-response -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install flonat/flonat-research review-response --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/review-response .claude/skills/review-response && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-response
GitHub stars
146
Token cost
~3k tokens
SKILL.md length
1,223 words
Files
6 (incl. references)
Skills in repo
83
Repo updated
First seen
Licence
MIT

At a glance

Systematic reviewer response workflow: parse comments, classify by severity, develop response strategy, write structured rebuttal.

  • Works in 5 steps: Parse and Classify → Develop Response Strategy → Write Responses → …
  • Asked to write rebuttal
  • SKILL.md covers When to Use, Workflow, Step 1: Parse and Classify and Step 2: Develop Response…, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Response is an agent skill from flonat/flonat-research. Systematic reviewer response workflow: parse comments, classify by severity, develop response strategy, write structured rebuttal. Use when asked to 'write rebuttal', 'respond to reviewers', 'draft review response', or 'handle R&R'.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/figure-interpretation.md`, `references/rebuttal-templates.md` and `references/response-strategies.md`).

It sits in Research & Science. The repository describes itself as: Shareable Claude Code + Codex infrastructure for PhD researchers — skills, agents, hooks, and rules for academic workflows. The licence is MIT.

When your agent uses it

  • Asked to write rebuttal
  • Respond to reviewers
  • Draft review response

Example prompts

  • “write rebuttal”
  • “respond to reviewers”
  • “draft review response”
  • “/review-response”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Parse and Classify
  2. Develop Response Strategy
  3. Write Responses
  4. Tone Check
  5. Assemble Rebuttal Document

What it can do on your machine

Read from SKILL.md and the folder at commit da27600. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Response loads about 3k tokens when it runs, and up to ~8.9k if it reads all its reference files. Until then it costs about 62 tokens; SKILL.md has 1,223 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from flonat/flonat-research at commit da27600, republished under its MIT licence (© flonat). 1,223 words, ~2,967 tokens.

Download SKILL.mdSave it as .claude/skills/review-response/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
review-response
description
Systematic reviewer response workflow: parse comments, classify by severity, develop response strategy, write structured rebuttal. Use when asked to 'write rebuttal', 'respond to reviewers', 'draft review response', or 'handle R&R'.
tags
Research, Academic, Rebuttal, Paper Writing
version
1.0.0
skill-dependencies
proofread

Review Response

Systematic workflow for responding to reviewer comments on academic papers. Covers the full cycle from parsing comments through to a polished rebuttal document.

When to Use

  • "Help me write a rebuttal"
  • "Respond to reviewer comments"
  • "Handle this R&R"
  • "Develop a review response strategy"
  • Paper has received referee reports and needs a structured response

Workflow

1. Receive reviewer comments
2. Parse and classify each comment (Major / Minor / Typo / Misunderstanding)
2b. Re-audit the paper against its artifact (empirical papers)
3. Develop response strategy per comment (Accept / Defend / Clarify / Experiment)
4. Write structured responses
5. Tone check — every response must pass the tone checklist
6. Assemble final rebuttal document

Step 1: Parse and Classify

Read all reviewer comments and classify each one:

TypeDefinitionPriority
MajorCore methodology, experimental design, results interpretation — requires substantive revision or new analysisHigh
MinorClarifications, presentation improvements, additional discussion — does not affect core contributionMedium
TypoSpelling, grammar, formatting, reference errorsLow
MisunderstandingReviewer misread or missed something already in the paper — needs polite clarificationHigh

Keyword signals for classification:

  • Major: "major concern", "fundamental issue", "missing experiments", "insufficient evidence", "not convincing"
  • Minor: "minor concern", "could be improved", "please clarify", "suggestion"
  • Typo: "typo", "grammar", "formatting", "inconsistent"
  • Misunderstanding: "The authors did not..." (but they did), "It is unclear..." (but it is stated)

Priority order: Major > Misunderstanding > Minor > Typo

Present the full classification table to the user before proceeding to strategy.

Step 1b: Re-audit the paper against its artifact (empirical papers)

Before assigning any strategy, check the submitted paper against the code, the run outputs and the replication package. Reviewers see the artifact too. A rebuttal that defends a claim the artifact contradicts does more damage than the original error.

  • Every reviewer claim about the artifact. Check each one directly against the files (model, framework, environment, "the code does not do X"). Classify it as reviewer-correct, reviewer-misread or ambiguous in the paper, with file:line evidence.
  • Qualitative methods claims. Check the paper's descriptions of tools, models, what was logged, procedures performed and pre-registration status against the code and artifacts. Treat protocol or plan documents as the source of those claims, never as evidence for them.
  • Headline numbers. Recompute them from the saved outputs, and spot-read the raw records behind any extraction or matching step (parsers, path or ID normalisers). Treat any anomaly a reviewer points to, or any count that looks too convenient, as a lead to follow.

If the re-audit finds a genuine error, stop and present it to the user before drafting, with the corrected figures and the claims that no longer hold. Whether and how to disclose it is his decision. When the audit was prompted by a reviewer's scrutiny, credit that scrutiny without implying the reviewer found the error. Trigger incident: 2026-09-23 ICSE #1402. A re-audit prompted by one reviewer's code inspection found a result-changing bug and four protocol-vs-implementation drifts that 25 logged pre-submission review passes (12 distinct checks) had missed.

Step 2: Develop Response Strategy

For each classified comment, assign a strategy:

StrategyWhen to Use
AcceptComment is valid, fix is feasible and improves the paper
DefendCurrent approach has sound justification; provide evidence and reasoning
ClarifyReviewer missed or misread existing content; point to it politely
ExperimentReviewer requests additional analysis that is feasible and would strengthen the paper

Decision flow:

Comment → Is the reviewer correct?
  Yes → Is the fix feasible?
    Yes → Accept
    No  → Accept principle + explain constraint + offer alternative
  Partially → Accept valid part + Defend the rest with evidence
  No (misunderstanding) → Clarify with specific location references
  Requests new work → Is it feasible?
    Yes → Experiment
    No  → Explain limitation + offer alternative analysis

Strategy combinations (common in practice):

  • Accept + Clarify: "We agree and have expanded Section 3. We also note this was partially addressed in Table 2."
  • Defend + Experiment: "Our choice of X is justified because [reasons], but we have also added the comparison with Y as requested."

Full strategy library with templates: references/response-strategies.md

Step 3: Write Responses

Each response follows this structure:

markdown
**Comment N.M**: [Reviewer's original comment, quoted verbatim]

**Response**: [Our substantive reply]

**Changes**: [Specific modifications with section/page/table references]

Non-negotiable rules:

  1. Every comment gets a response — even typos
  2. Every response starts with thanks
  3. Every claim in the response has evidence (data, citation, or specific manuscript reference)
  4. Every change states its location (Section X, page Y, Table Z)
  5. Never say "The reviewer is wrong" — use "We would like to respectfully clarify"
  6. Every "Changes:" entry is verified against the manuscript before the letter is assembled — read/grep the actual .tex at the cited location; never trust the comment tracker, an earlier letter draft, or memory of the edit. A promised change with no matching manuscript edit is either downgraded to an honest acknowledgment ("we discuss rather than change...") or flagged to the user as unfulfilled — a letter must never describe the manuscript as more revised than it is. Record the outcome per commitment in the tracker's Fulfillment Ledger (fulfilled / partial / not_fulfilled / acknowledgment_only; see templates/referee-comments/comment-tracker.md).
Show full SKILL.md (518 more words)Show less

Step 4: Tone Check

Before finalising, run every response through this checklist:

  • Opens with genuine thanks (not perfunctory)
  • Uses "We" not "I"
  • No defensive or aggressive language
  • No "obviously", "clearly", or "it is well-known"
  • Specific location references for all changes
  • No vague promises ("We will..." without specifics)
  • Misunderstandings addressed with "We apologise for the confusion" not "The reviewer failed to notice"

Full tone guide with good/bad examples: references/tone-guidelines.md

Step 5: Assemble Rebuttal Document

Standard structure
markdown
# Response to Reviewers

We sincerely thank all reviewers for their valuable feedback. We have carefully
addressed all comments and made substantial revisions. Below we provide detailed
responses to each reviewer's comments.

---

## Response to Reviewer 1

### Major Comments

**Comment 1.1**: [verbatim]
**Response**: [reply]
**Changes**: [locations]

### Minor Comments

**Comment 1.2**: [verbatim]
**Response**: [reply]
**Changes**: [locations]

---

## Response to Reviewer 2
[same structure]

---

## Summary of Major Changes

1. [Change 1] (addressing Reviewer X, Comment Y)
2. [Change 2] (addressing Reviewers X and Z)
3. ...

We believe these revisions have significantly strengthened the manuscript.
Where multiple reviewers raise the same issue

Consolidate: "We thank Reviewers 1 and 3 for raising this important point. We have [action] which addresses both concerns."

Where reviewers contradict each other

Acknowledge both perspectives: "After careful consideration, we have [chosen approach] because [reasons], which we believe addresses both reviewers' core concerns."

Full template library: references/rebuttal-templates.md

Output

The skill produces a complete rebuttal document saved to the project's correspondence/ directory (or paper directory if no correspondence folder exists).

Naming convention: rebuttal-{venue}-{round}.md (e.g., rebuttal-jmp-r1.md)

Templates

Three templates live under templates/referee-comments/. Use them together across a revision cycle:

TemplatePurposeWhen
reviewer-comments-verbatim.texInternal landscape doc with reviewer text quoted verbatim, one row per comment ID (R1-C1, R2-C3, …)First, on receipt of reports
comment-tracker.mdTriage + patch-plan table — type/priority/action/owner/status per comment IDDuring planning (Steps 1–2)
response-letter-ansrev.texDefault LaTeX scaffold for the response letter. Uses the ansrev package: auto-numbers reviewers and comments, pulls labels/citations/quotes from the main paper via xrWhen writing the formal response (Steps 3–5)
When to use the LaTeX scaffold vs. the Markdown rebuttal
  • Markdown rebuttal (the default rebuttal-{venue}-{round}.md above) — for venues that accept Markdown/plain-text uploads, internal review by co-authors, or quick R&Rs.
  • LaTeX response-letter-ansrev.tex — when the venue requires a typeset PDF response, when the main paper has many labels/citations the response needs to reference, or when reviewers will be assigned numbers and cross-referenced ("see our reply to Reviewer 1 Comment 3"). Strongly preferred for OR / Management Science / journals with structured R&R.
Using response-letter-ansrev.tex
  1. Copy templates/referee-comments/response-letter-ansrev.tex into the project's correspondence/ (or paper-{venue}/paper/) directory.
  2. Copy templates/referee-comments/ansrev/{ansrev.sty,revquote.sty} next to it — these are vendored from GitHub (not on CTAN). See templates/referee-comments/ansrev/README.md for provenance.
  3. Keep the canonical project .latexmkrc unchanged. Copy templates/referee-comments/ansrev/.latexmkrc.local beside it so latexmk auto-recompiles the main paper for xr cross-refs. If .latexmkrc.local already exists, merge the custom dependency into that supplement rather than overwriting it.
  4. Set main={<main-file-basename>} in the scaffold's \usepackage{ansrev} options.
  5. Compile the main paper first, then the response file. Refer to comment-tracker IDs (R1-C1, AE-C2) as \label{}s inside each \QA{}{} — \ref{R1-C1} elsewhere expands to "Reviewer 1 Comment #1".

Integration

  • Before writing: Read the reviewer comments and the paper to understand context
  • During writing: Cross-reference the paper for specific section/page numbers
  • After writing: Run proofread on the rebuttal document itself for tone and clarity
  • If paper changes are needed: Track them separately — the rebuttal documents the response, not the revision

Reference Documents

  • references/review-classification.md — Classification criteria with keyword signals
  • references/response-strategies.md — Strategy library with templates for each type
  • references/rebuttal-templates.md — Full rebuttal document templates
  • references/tone-guidelines.md — Tone guide with good/bad expression pairs
  • references/figure-interpretation.md — Figure interpretation guide (useful when discussing figures in responses)

Adapted from

galaxy-dawn/claude-scholar review-response skill, adapted for general academic research (not ML-specific).

© flonat, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in skills/review-response of flonat/flonat-research.

  • SKILL.md
  • references/figure-interpretation.md
  • references/rebuttal-templates.md
  • references/response-strategies.md
  • references/review-classification.md
  • references/tone-guidelines.md

Open the folder on GitHubat commit da27600

Compare with similar skills

Review Response next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Questions about Review Response

What does Review Response do?

Systematic reviewer response workflow: parse comments, classify by severity, develop response strategy, write structured rebuttal. Review Response is an agent skill from flonat/flonat-research. Systematic reviewer response workflow: parse comments, classify by severity, develop response strategy, write structured rebuttal.

When should I use Review Response?

Review Response fits situations like: asked to write rebuttal; respond to reviewers; draft review response.

How do I install Review Response in Claude Code?

Run `npx skills add flonat/flonat-research --skill review-response -a claude-code`. Or copy the skill folder (skills/review-response in flonat/flonat-research) into .claude/skills/review-response in your project. Claude Code loads it when a task matches its description.

How do I install Review Response in Codex?

Run `npx skills add flonat/flonat-research --skill review-response -a codex`. Or copy the skill folder (skills/review-response in flonat/flonat-research) into .agents/skills/review-response in your project. Codex loads it when a task matches its description.

Can I use Review Response in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add flonat/flonat-research --skill review-response -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-response, .gemini/skills/review-response, .github/skills/review-response and .opencode/skills/review-response in your project.

What does Review Response need to run?

SKILL.md names no scripts, command-line tools or credentials: Review Response is instructions for the agent only.

Does Review Response access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Response safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review Response use?

Review Response is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Response use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.9k tokens, read only when the agent opens those files.

What are the alternatives to Review Response?

Skills that share tags, products or a category with Review Response: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Response?

flonat (a GitHub user) maintains it in flonat/flonat-research, which has 146 GitHub stars. The repository holds 83 skills in this directory. The repository was last updated on September 29, 2026.

Source: flonat/flonat-research on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.