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/deepxivWrote 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/deepxiv)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/deepxiv"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/deepxiv/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/deepxiv"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/deepxiv.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00040 | $0.02369 |
| Opus 5 | $0.00020 | $0.01184 |
| Sonnet 5 | $0.00008 | $0.00474 |
| Haiku 4.5 | $0.00004 | $0.00237 |
Grade A, and why
deepxiv 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.
How it starts
The opening of the file, as written. The whole thing — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DeepXiv Paper Search & Progressive Reading
Search topic or paper ID: $ARGUMENTS
Role & Positioning
DeepXiv is the progressive-reading literature source:
| Skill | Source | Best for |
|---|---|---|
/arxiv |
arXiv API | Batch search, PDF download, metadata |
/deepxiv |
DeepXiv SDK | Progressive section-level reading |
/semantic-scholar |
S2 API | Published venue metadata, citation counts |
/alphaxiv |
alphaxiv.org | Instant LLM-optimized summary of one paper, with LaTeX source fallback |
Use DeepXiv when you want to avoid loading full papers too early.
Constants
- DEEPXIV_FETCHER — canonical name
deepxiv_fetch.py, resolved pershared-references/integration-contract.md§2 (Policy D1 — primary + fallback cascade). If unresolved (canonical chain exhausted), fall back to the rawdeepxivCLI (documented per command below). - MAX_RESULTS = 10 — Default number of results to return.
Overrides (append to arguments):
/deepxiv "agent memory" - max: 5— top 5 results/deepxiv "2409.05591" - brief— quick paper summary/deepxiv "2409.05591" - head— metadata + section overview/deepxiv "2409.05591" - section: Introduction— read one section only/deepxiv "trending" - days: 14 - max: 10— trending papers/deepxiv "karpathy" - web— DeepXiv web search/deepxiv "258001" - sc— Semantic Scholar metadata by ID
Setup
DeepXiv is optional. If the CLI is not installed, tell the user:
pip install deepxiv-sdk
On first use, deepxiv auto-registers a free token and stores it in ~/.env.
Workflow
Step 1: Parse Arguments
Parse $ARGUMENTS for:
- Query or ID: a paper topic, arXiv ID, or Semantic Scholar ID
- max: N: overrideMAX_RESULTS- brief: fetch paper brief- head: fetch metadata and section map- section: NAME: fetch one named section- trendingor querytrending: fetch trending papers- days: 7|14|30: trending time window- web: run DeepXiv web search- sc: fetch Semantic Scholar metadata by ID
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 · 264 lines · 40 tokens per session scan A cbf5e46d7140
deepxiv 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 40 tokens to every session and 2,369 once invoked, about $0.0002 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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