Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add flonat/flonat-research --skill review-responsegit clone --depth 1 https://github.com/flonat/flonat-researchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/flonat/flonat-research/review-response)<a href="https://agentmods.dev/skills/flonat/flonat-research/review-response"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/review-response/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/flonat/flonat-research/review-response"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/review-response.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.02333 |
| Opus 5 | $0.00025 | $0.01167 |
| Sonnet 5 | $0.00010 | $0.00467 |
| Haiku 4.5 | $0.00005 | $0.00233 |
Grade A, and why
review-response scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
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:
| Type | Definition | Priority |
|---|---|---|
| Major | Core methodology, experimental design, results interpretation — requires substantive revision or new analysis | High |
| Minor | Clarifications, presentation improvements, additional discussion — does not affect core contribution | Medium |
| Typo | Spelling, grammar, formatting, reference errors | Low |
| Misunderstanding | Reviewer misread or missed something already in the paper — needs polite clarification | High |
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 2: Develop Response Strategy
For each classified comment, assign a strategy:
| Strategy | When to Use |
|---|---|
| Accept | Comment is valid, fix is feasible and improves the paper |
| Defend | Current approach has sound justification; provide evidence and reasoning |
| Clarify | Reviewer missed or misread existing content; point to it politely |
| Experiment | Reviewer requests additional analysis that is feasible and would strengthen the paper |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 220 lines · 50 tokens per session scan A 1b64daf8c2d0
review-response is a skill published in the GitHub repository flonat/flonat-research (132 stars, last pushed 15d ago), licensed MIT. It adds 50 tokens to every session and 2,333 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
review-response
Systematic review response workflow from comment analysis to professional rebuttal writing. Use when the user asks to "write rebuttal", "respond to reviewers", "draft review response", or "analyze review comments". Improves paper acceptance rates.
fin-paper-plan
Generate structured paper outline adapted to target journal.
claim-audit
Audit what a passing script actually established, before writing any prose about it — build the computed-object ledger, rewrite every check's label as the weakest statement that makes its body pass, and separate the verdict on someone else's work from your own new claim. Run after the script passes and BEFORE the…
doc-audit
Evaluate one project document in detail — a workbook section, big picture, strategy map, handoff message, paper, brief, or primer — without editing it. Runs the mechanical lint, builds a claim ledger, types every headline against the eight claim statuses in CLAUDE.md, traces cited evidence to the scripts and logs that…
latex-compile
Compile a LaTeX document and fix every error plus aesthetic issue (overfull/underfull boxes, widows, alignment, fonts) for a clean PDF and log. Use this instead of running pdflatex/latexmk manually — it avoids the latexmk stale-log trap and silent grep failures on binary log output, and it reformats rather than…
nb-to-wolfbook
Convert Mathematica .nb or .m files to Wolfbook .wb format so they open and run in VS Code. Use when bringing existing .nb/.m files into Wolfbook, or to make an existing .wb bridge-safe.