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/research-reviewWrote 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/research-review)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-review"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-review/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/research-review"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-review.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 | $0.00054 | $0.02916 |
| Opus 5 | $0.00027 | $0.01458 |
| Sonnet 5 | $0.00011 | $0.00583 |
| Haiku 4.5 | $0.00005 | $0.00292 |
Grade A, and why
research-review 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 today.
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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Review via External Reviewer Backend (ultra reasoning)
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It is verdict-bearing — it produces a cross-model review verdict, multi-round with reviewer thread continuity. An external timer re-fires the verdict on wall-clock time and breaks the reviewer's round-to-round memory: zero new signal, full token cost. Schedule the external wait that precedes it (work ready → then review once), not the verdict. Seeshared-references/external-cadence.md.
Get a multi-round critical review of research work from the selected external reviewer backend with maximum reasoning depth.
Constants
- REVIEWER_MODEL =
gpt-6-astra— Default model for the Codex backend, reasoning effortultra(deep-audit tier). Must be an OpenAI model (e.g.,gpt-6-astra,gpt-5.5,o3). Manual backend uses a model the user chooses — it must be a recognized model from a different family (OpenAI, Anthropic, Google, DeepSeek, Moonshot/Kimi, Qwen). - REVIEWER_BACKEND =
codex— Default: Codex MCP (ultra). Override with— reviewer: oracle-profor Oracle MCP, or— reviewer: manualfor Manual Review MCP. If manual-review MCP is unavailable, stop and print the install command; do not fall back to Codex. Seeshared-references/reviewer-routing.md.
Reviewer Calling Convention
When calling the reviewer, branch on REVIEWER_BACKEND:
If REVIEWER_BACKEND = codex:
Use mcp__codex__codex for new review threads.
Use mcp__codex__codex-reply for follow-up rounds (reuse threadId).
If REVIEWER_BACKEND = manual:
Use mcp__manual_review__review for new review threads with:
prompt: [exact same prompt that would go to Codex]
config: {"model_reasoning_effort": "xhigh", "executor_model": "", "require_reviewer_model": true}
Save the returned threadId.
Use mcp__manual_review__review_reply for follow-up rounds with:
threadId: [saved manual-review threadId]
prompt: [follow-up prompt]
config: {"model_reasoning_effort": "xhigh", "executor_model": "", "require_reviewer_model": true}
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.
- today Changed 85c75d2bef1f
- 5d ago Changed 41df82cf9e5a
- 9d ago First seen · 224 lines · 54 tokens per session scan A 96e299d65e44
research-review 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 54 tokens to every session and 2,916 once invoked, about $0.0003 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 2026-09-03.
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