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 EvoScientist/EvoSkills --skill paper-rebuttalgit clone --depth 1 https://github.com/EvoScientist/EvoSkillsWrote 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/evoscientist/evoskills/paper-rebuttal)<a href="https://agentmods.dev/skills/evoscientist/evoskills/paper-rebuttal"><img src="https://agentmods.dev/badge/skills/evoscientist/evoskills/paper-rebuttal/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/evoscientist/evoskills/paper-rebuttal"><img src="https://agentmods.dev/badge/skills/evoscientist/evoskills/paper-rebuttal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00127 | $0.02094 |
| Opus 5 | $0.00063 | $0.01047 |
| Sonnet 5 | $0.00025 | $0.00419 |
| Haiku 4.5 | $0.00013 | $0.00209 |
Grade A, and why
paper-rebuttal 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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- paper-rebuttal — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Rebuttal
A systematic approach to writing rebuttals after receiving peer review feedback. The goal is not to defend every point — it's to move scores by addressing the concerns that actually drive them.
When to Use This Skill
- User received reviewer comments and needs to write a rebuttal
- User asks how to respond to specific reviewer criticism
- User wants to analyze reviews strategically before responding
- User mentions "rebuttal", "reviewer comments", "review feedback", "respond to reviewers"
For pre-submission self-review and catching weaknesses before they become reviewer complaints, use the
paper-reviewskill.
Step 1: Diagnose Reviews
Before writing a single word, answer: "Why did this reviewer give this exact score?" Not what they wrote — what drove the score. Most researchers skip this and address every comment equally. That is a mistake.
Score Diagnosis
For each reviewer, ask: "What would move this reviewer from their current score to acceptance?"
| Score Range | Typical Situation | Your Strategy |
|---|---|---|
| 7+ | Already your champion | Arm them with ammunition for the discussion phase |
| 5-6 | On the fence, 1-2 concerns holding them back | Identify and resolve those specific concerns |
| 3-4 | Fundamental objection | Determine if the objection is addressable; if not, focus elsewhere |
Color-Code Every Comment
Read through each review and mark every comment:
| Color | Meaning | Action | Budget |
|---|---|---|---|
| Red | Score-driving concern — this is why the score is low | Address first, maximum effort and evidence | 60% |
| Orange | Addressable concern — can be resolved | Respond with concrete data or revision | 30% |
| Gray | Minor or cosmetic | Acknowledge briefly, confirm fix | 10% |
| Green | Positive comment or praise | Note as ammunition for your champion | — |
Identify the Invisible Question
Behind every reviewer comment is an unspoken question. A comment like "The baselines are outdated" really asks: "Is this method actually competitive with current approaches?" Address the invisible question, not just the surface request.
What ships with it
2 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.
- 11d ago First seen · 160 lines · 127 tokens per session scan A 1add324da299
paper-rebuttal is a skill published in the GitHub repository EvoScientist/EvoSkills (436 stars, last pushed 10d ago), licensed Apache-2.0. It adds 127 tokens to every session and 2,094 once invoked, about $0.0006 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 2026-08-30.
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