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/openalexWrote 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/openalex)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/openalex"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/openalex/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/openalex"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/openalex.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 64 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 67 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00055 | $0.02645 |
| Opus 5 | $0.00028 | $0.01323 |
| Sonnet 5 | $0.00011 | $0.00529 |
| Haiku 4.5 | $0.00006 | $0.00265 |
Grade A, and why
openalex 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 11d 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:
- openalex — 89% identical, 50 lines differ
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenAlex Academic Search
Search query: $ARGUMENTS
Role & Positioning
This skill uses OpenAlex as a comprehensive open academic graph source:
| Skill | Source | Best for |
|---|---|---|
/arxiv |
arXiv API | Latest preprints, cutting-edge unrefereed work |
/semantic-scholar |
Semantic Scholar API | Published venue papers (IEEE, ACM, Springer) with citation counts |
/openalex |
OpenAlex API | Open citation graph, institutional affiliations, funding data, comprehensive metadata |
/deepxiv |
DeepXiv CLI | Layered reading: search, brief, section map, section reads |
/exa-search |
Exa API | Broad web search: blogs, docs, news, companies, research papers |
/gemini-search |
Gemini MCP / CLI | AI-powered broad literature discovery |
Use OpenAlex when you want:
- Open citation data — fully open citation graph (no API key required for basic use)
- Institutional affiliations — author institutions and collaborations
- Funding information — NSF, NIH, and other funding sources
- Comprehensive metadata — topics, keywords, abstract, open access status
- Cross-database coverage — indexes 250M+ works from multiple sources
Constants
- MAX_RESULTS = 10 — Default number of results. Override with
— max: 20. - DEFAULT_SORT = relevance — Sort by relevance. Override with
— sort: citationsor— sort: date. - OPENALEX_FETCHER — canonical name
openalex_fetch.py, resolved pershared-references/integration-contract.md§2 (Policy D1 — standalone/openalexhas no documented inline fallback, so unresolved helper terminates with an explicit error).
Overrides (append to arguments):
/openalex "topic" — max: 20— return up to 20 results/openalex "topic" — year: 2023-— papers from 2023 onward/openalex "topic" — year: 2020-2023— papers from 2020 to 2023/openalex "topic" — type: article— only journal articles/openalex "topic" — type: preprint— only preprints/openalex "topic" — open-access— only open access papers/openalex "topic" — min-citations: 50— minimum 50 citations/openalex "topic" — sort: citations— sort by citation count (descending)/openalex "topic" — sort: date— sort by publication date (newest first)
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.
- 11d ago First seen · 238 lines · 55 tokens per session scan A 4c853b646987
openalex is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (15,970 stars, last pushed 2d ago), licensed MIT. It adds 55 tokens to every session and 2,645 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 2026-08-30.
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ml-training-recipes
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