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 yuangao-tum/rebuttal-skills --skill write-rebuttalgit clone --depth 1 https://github.com/yuangao-tum/rebuttal-skillsWrote 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/yuangao-tum/rebuttal-skills/write-rebuttal)<a href="https://agentmods.dev/skills/yuangao-tum/rebuttal-skills/write-rebuttal"><img src="https://agentmods.dev/badge/skills/yuangao-tum/rebuttal-skills/write-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/yuangao-tum/rebuttal-skills/write-rebuttal"><img src="https://agentmods.dev/badge/skills/yuangao-tum/rebuttal-skills/write-rebuttal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00141 | $0.01536 |
| Opus 5 | $0.00071 | $0.00768 |
| Sonnet 5 | $0.00028 | $0.00307 |
| Haiku 4.5 | $0.00014 | $0.00154 |
Grade A, and why
write-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 12d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write Rebuttal
Distilled from Devi Parikh, Dhruv Batra & Stefan Lee, How we write rebuttals (https://deviparikh.medium.com/how-we-write-rebuttals-dc84742fece1).
A rebuttal exists to clarify and convince — it is one stage in a deliberative scientific process, not a fight to win. Optimize it for the reader, not for venting.
The one idea that governs everything
Two audiences, and beginners forget the second one:
- Reviewers — read your paper, may have forgotten details. Goal: clarify doubts, correct misunderstandings, push back on mischaracterizations, incorporate feedback.
- Area Chair / meta-reviewer — likely has not read your paper closely and won't re-read it. Goal: show good-faith effort, give a fair summary, make it obvious each concern was addressed, flag bad-faith reviewing, and help them decide.
Most new researchers write only for (1). Writing for (2) is what moves decisions. Think of it like debate: you convince the judges (AC), not your opponents.
The acceptance test for every response — the "neutral third-party" test: Could someone who has not read your paper or the reviews tell, from your rebuttal alone, that the concern was addressed? If not, rewrite it (self-contained; Tip 8).
Process (do these in order)
- Itemize — put every reviewer comment/question/concern in a spreadsheet, one row each, columns per reviewer. Do this first, ASAP: it guarantees nothing is missed, surfaces shared concerns to consolidate, and identifies needed experiments early while there's still time to run them.
- Brain-dump — rough responses in the sheet, no style/length worry. Collaborative.
- Draft — turn the consensus into concise responses covering every point.
- Revise — reread the reviews, check completeness, prioritize the majors, fit the space limit. Run the neutral-third-party test on each response.
See references/rebuttal-skeleton.md for a ready structure (summary → common concerns → per-reviewer → note to AC), micro-templates, and two AC "dashboard" summary tables. assets/rebuttal-template.tex is the compilable LaTeX implementation — use it only where free-form PDF responses are accepted.
What ships with it
4 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.
- 12d ago First seen · 96 lines · 141 tokens per session scan A 5c3ca3acfd9d
write-rebuttal is a skill published in the GitHub repository yuangao-tum/rebuttal-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 141 tokens to every session and 1,536 once invoked, about $0.0007 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 2026-08-31.
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