ARIS is a collection of Markdown-based skills that define a workflow for autonomous machine-learning research, including idea discovery, experiment automation, and review loops. Researchers and AI coding agents use it across tools such as Claude Code, Codex, Cursor, and OpenClaw without depending on a single framework. The catalogue entries are ARIS workflow skills and agents.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleepnpx agentmods add skills/wanshuiyin/auto-claude-code-research-in-sleep/kill-argumentWrote 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/wanshuiyin/auto-claude-code-research-in-sleep/kill-argument)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/kill-argument"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/kill-argument/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/wanshuiyin/auto-claude-code-research-in-sleep/kill-argument"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/kill-argument.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00088 | $0.06088 |
| Opus 5 | $0.00044 | $0.03044 |
| Sonnet 5 | $0.00018 | $0.01218 |
| Haiku 4.5 | $0.00009 | $0.00609 |
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 3d 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:
- kill-argument — 86% identical, 87 lines differ
How it starts
The opening of the file, as written. The whole thing — 438 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kill Argument Exercise: Adversarial Attack-Defense Review
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It is verdict-bearing — it produces an adversarial accept/reject verdict (attack → adjudication). Re-firing it on a wall-clock timer adds no new signal (the attack changes only when the paper changes). Schedule the external wait that precedes it — draft stable → then run this once before submission. Seeshared-references/external-cadence.md.
Stress-test the headline claims of a paper against the strongest possible rejection argument: $ARGUMENTS
Why This Exists
Standard score-based reviews (/research-review, /auto-paper-improvement-loop) tend to produce balanced weakness lists. Each weakness gets ~equal attention, ranked CRITICAL > MAJOR > MINOR. Empirically, this misses one specific failure mode: the single most damaging argument a reviewer would write in a rejection paragraph — the one sentence that, if a senior area chair reads it, kills the paper.
A balanced reviewer might list "scope-overclaim risk" as MAJOR alongside 3-5 other MAJORs, never quite committing. An adversarial reviewer must commit: their entire job is to convince the area chair to reject in 200 words.
This skill runs that adversarial pass deliberately, then forces a second fresh reviewer to defend point-by-point, classify each rejection as already-fixed / partially-fixed / still-unresolved, and surface what's actually load-bearing.
Empirical motivation: in a real submission run, after several rounds of standard improvement (score 7-8/10), the kill-argument exercise surfaced framing weaknesses that no prior review caught (e.g., a setting being mostly conditional rather than truly general, or a baseline being irrelevant to real systems). Author rebuttal forced explicit scope qualifications in abstract and discussion that weren't visible from the score-based reviews alone.
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.
- 3d ago Changed 7a222081ddd9
- 11d ago First seen · 438 lines · 88 tokens per session scan A 074e7cb2baa9
kill-argument is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (15,970 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 6,088 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.
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