kill-argument

kill-argument is a skill for Claude Code from AI4Scientist/nano-scientist. It costs 88 tokens per session (5,018 once invoked), scanned A, original, no licence file.

A two-stage review process that first writes the strongest case for rejecting a research paper, then answers that case point by point and identifies serious issues still left unresolved.

In plain words
What is it for?
Use it for hostile paper reviews, rebuttal preparation, or testing whether a research argument can withstand a skeptical reviewer.
Why use it?
It exposes weaknesses before reviewers do and separates problems that can be answered from problems that need more work.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it for hostile paper reviews, rebuttal preparation, or testing whether a research argument can withstand a skeptical reviewer.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ai4scientist/nano-scientist/kill-argument
Install

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.

Any agent
npx skills add AI4Scientist/nano-scientist --skill kill-argument
Clone the repo
git clone --depth 1 https://github.com/AI4Scientist/nano-scientist

Made for: Claude Code.

Wrote 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.

agentmods badge for kill-argument

README.md
[![agentmods](https://agentmods.dev/badge/skills/ai4scientist/nano-scientist/kill-argument.svg)](https://agentmods.dev/skills/ai4scientist/nano-scientist/kill-argument)
Your own site
<a href="https://agentmods.dev/skills/ai4scientist/nano-scientist/kill-argument"><img src="https://agentmods.dev/badge/skills/ai4scientist/nano-scientist/kill-argument.svg" alt="Measured on agentmods" height="20"></a>
Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,018 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00088 $0.05018
Opus 5 $0.00044 $0.02509
Sonnet 5 $0.00018 $0.01004
Haiku 4.5 $0.00009 $0.00502

Measured 8d ago against content hash b0a23bb1e766, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

kill-argument 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 8d 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.

skills/kill-argument/SKILL.md · 387 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 8d ago First seen · 387 lines · 88 tokens per session scan A b0a23bb1e766

Subscribe to this mod's changes

kill-argument is a skill published in the GitHub repository AI4Scientist/nano-scientist (127 stars, last pushed 3mo ago), with no licence file. It adds 88 tokens to every session and 5,018 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

baseline-comparison-audit

Audit whether a paper's baseline comparisons are COMPLETE, FAIR, and SIGNIFICANT: a required recent SOTA baseline is missing while 'best/SOTA' is claimed (HP-MISSING-BASELINE); a baseline is undertuned / given less compute-tuning-data, run at a mismatched config, or the equal-budget ablation-as-baseline is absent…

wanshuiyin/Anti-Autoresearch · 310 tokens

eval-design-forensics

Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan…

wanshuiyin/Anti-Autoresearch · 458 tokens

novelty-duplication-advisory

MEMO-ONLY prior-work overlap advisory: surfaces the two ADVISORY taxonomy signals neither a tool nor a model can decide from the paper alone — ADV-TRIVIAL-COMBINATION (standard A+B+C / 缝合 stapling) and ADV-DUPLICATE-PUBLICATION (repackaged / duplicate submission). The executor RETRIEVES candidate prior work (DBLP…

wanshuiyin/Anti-Autoresearch · 284 tokens

adversarial-case-builder

Synthesize the single strongest EVIDENCE-BOUND reviewer case to reject a paper, built ONLY from the evidence ledger (claims.json) + the other auditors' confirmed findings — never free-floating LLM critique. Two fresh cross-model codex threads: an attack writes the 200-word rejection paragraph (every accusation tagged…

wanshuiyin/Anti-Autoresearch · 194 tokens

ai-style-impressions

Transparent, itemized impressions of AI-generated WRITING STYLE — the repo's ONLY non-integrity track. Two passes: a deterministic defensive-hedge density screen (tools/checkaistyle.py, AIS-DEFENSIVE-HEDGE) plus a fresh cross-model GROSS-cases-only semantic pass over the 13 AIS- style tells (broken narrative arc, LLM…

wanshuiyin/Anti-Autoresearch · 284 tokens

citation-forensics

Citation-integrity forensics: is every reference real, correctly attributed, and used in a context the cited work actually supports? Catches hallucinated references (no paper at the claimed arXiv id/DOI/venue, fabricated authors/year), metadata drift (wrong year/venue/version), and wrong-context citations (a real…

wanshuiyin/Anti-Autoresearch · 193 tokens