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/paper-claim-auditWrote 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/paper-claim-audit)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-claim-audit"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-claim-audit/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/paper-claim-audit"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-claim-audit.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 | $0.00077 | $0.03585 |
| Opus 5 | $0.00039 | $0.01792 |
| Sonnet 5 | $0.00015 | $0.00717 |
| Haiku 4.5 | $0.00008 | $0.00359 |
Grade A, and why
paper-claim-audit 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 5d 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-claim-audit — 89% identical, 46 lines differ
How it starts
The opening of the file, as written. The whole thing — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Claim Audit: Zero-Context Evidence Verification
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It is verdict-bearing — it judges paper-to-evidence fidelity with a deliberately zero-context fresh reviewer. Re-firing that verdict on a wall-clock timer adds no new signal (it changes only when the paper or results change). Schedule the external wait that precedes it — paper draft ready → then audit once. Seeshared-references/external-cadence.md.
Verify that every claim in the paper matches raw evidence for: $ARGUMENTS
Why This Exists
The executor writes experiments AND writes the paper. It "knows" what the results should be. This creates confirmation bias:
- Rounding 84.7% up to 85.3%
- Reporting best seed instead of average
- Citing metrics from a different experiment config
- Claiming "improves by 15%" when the delta is actually 12.8%
A fresh reviewer with zero prior context catches these because it has no expectations — it just compares paper text vs raw files.
How This Differs From Other Audit Skills
| Skill | Question it answers |
|---|---|
/experiment-audit |
Is the experiment code honest? (fake GT, normalization fraud) |
/result-to-claim |
Does the data scientifically support this claim? |
/paper-claim-audit |
Does the paper report the data truthfully and precisely? |
Core Principle
Zero-context, fresh reviewer. The auditor receives ONLY:
- Paper .tex files (the claims)
- Raw result files (the evidence)
It does NOT receive:
- ❌ EXPERIMENT_LOG.md
- ❌ EXPERIMENT_TRACKER.md
- ❌ AUTO_REVIEW.md
- ❌ NARRATIVE_REPORT.md
- ❌ Any executor summary or interpretation
- ❌ Any prior audit results
- ❌ Any conversation history
This is stricter than reviewer-independence — it's zero-context evidence audit.
Workflow
Step 1: Collect Files (Executor — Claude)
Locate paper and result files WITHOUT reading or interpreting them.
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.
- 5d ago Changed 7a6e76d6355a
- 13d ago First seen · 349 lines · 77 tokens per session scan A 7cf2a3659a0d
paper-claim-audit is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (16,030 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 3,585 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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