AutoSci is an AI research platform organized around a wiki, with an agent that supports stages of scientific work such as reading, experimentation, writing, and retaining knowledge across projects. It is for people building or using AI-assisted research workflows, with Claude Code, Codex, and OpenCode adaptations available. The catalogue add-ons extend those agent-specific workflows.
Borrowing it
Nothing to install: this file belongs to skyllwt/AutoSci. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/skyllwt/AutoSci/main/.claude/skills/rebuttal/SKILL.mdgit clone --depth 1 https://github.com/skyllwt/AutoSciWrote 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/skyllwt/autosci/rebuttal)<a href="https://agentmods.dev/skills/skyllwt/autosci/rebuttal"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/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/skyllwt/autosci/rebuttal"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/rebuttal.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 | $0.00034 | $0.03964 |
| Opus 5.5 | $0.00014 | $0.01586 |
| Sonnet 5.5 | $0.00007 | $0.00793 |
| Haiku 4.5 | $0.00003 | $0.00396 |
Grade A, and why
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 2mo 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 — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/rebuttal
Parse review comments, atomize each concern (Rvx-Cy numbering) and map it to a wiki idea or method, check whether evidence is sufficient (tracing back to wiki experiments), simulate reviewer follow-up questions with Review LLM (stress-test, scored 1-5), and generate a formal plain-text rebuttal and a rich-text rebuttal. Safety checks ensure no fabrication, no overpromise, full coverage.
Inputs
review: source of review comments, one of:- file path (e.g.
raw/reviews/reviewer1.txt,raw/reviews/meta-review.md) - multiple file paths (comma-separated:
raw/reviews/R1.txt,raw/reviews/R2.txt,raw/reviews/R3.txt) - directly pasted review text
- file path (e.g.
--paper-slug(optional): slug of the associated paper in wiki/outputs/, used to locate PAPER_PLAN--venue(optional): target conference/journal (ICLR / NeurIPS / ICML / ACL / CVPR); affects rebuttal format and word limits--stress-test(optional, enabled by default): Review LLM simulates reviewer follow-up; disable with--no-stress-test--format(optional, defaultformal): output formatformal: formal plain-text rebuttal (suitable for pasting directly into submission system)rich: rich-text version (with wiki [[links]], detailed analysis, improvement plan)
Outputs
- wiki/outputs/rebuttal-{slug}.md — rich-text rebuttal (with [[wikilinks]], evidence tracing, analysis tables)
- wiki/outputs/rebuttal-{slug}.txt — formal rebuttal (plain text, suitable for pasting into submission system)
- wiki/ideas/*.md / wiki/methods/*.md — if a concern exposes an evidence gap, append a suggestion to the relevant section (
## Risks/## Lessons learnedfor ideas;## Limitationsfor methods) - wiki/log.md — append log entry
Wiki Interaction
Reads
wiki/ideas/*.md— map concerns to ideas, check linked experiments and novelty argumentwiki/methods/*.md— map concerns to methods, check Mechanism / Procedure / Limitationswiki/experiments/*.md— find experiment results supporting ideas (vialinked_idea)wiki/papers/*.md— find citation context for referenced paperswiki/concepts/*.md— understand the conceptual background of method-related concernswiki/outputs/PAPER_PLAN.md— understand paper structure (from /paper-plan, if --paper-slug provided)wiki/graph/context_brief.md— global contextwiki/graph/edges.jsonl— idea-experiment-paper-method relationships.claude/skills/shared-references/cross-model-review.md— Review LLM stress-test independence
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.
- 2mo ago First seen · 345 lines · 34 tokens per session scan A 13beaf247273
rebuttal is a skill published in the GitHub repository skyllwt/AutoSci (1,706 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 3,964 once invoked, about $0.0001 per session on Opus 5.5. 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.
rebuttal compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| rebuttalskyllwt/AutoSci | Skill | 1.7k | 1mo ago | MIT | A | 34 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |