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/exa-searchWrote 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/exa-search)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/exa-search"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/exa-search/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/exa-search"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/exa-search.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 | $0.00049 | $0.02387 |
| Opus 5 | $0.00024 | $0.01193 |
| Sonnet 5 | $0.00010 | $0.00477 |
| Haiku 4.5 | $0.00005 | $0.00239 |
Grade A, and why
exa-search 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 12d 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exa AI-Powered Web Search
Search query: $ARGUMENTS
Role & Positioning
Exa is the broad web search source with built-in content extraction:
| Skill | Best for |
|---|---|
/arxiv |
Direct preprint search and PDF download |
/semantic-scholar |
Published venue papers (IEEE, ACM, Springer), citation counts |
/deepxiv |
Layered reading: search, brief, section map, section reads |
/exa-search |
Broad web search: blogs, docs, news, companies, research papers — with content extraction |
Use Exa when you need results beyond academic databases, or when you want content (highlights, full text, summaries) extracted alongside search results.
Constants
- EXA_FETCHER — canonical name
exa_search.py, resolved pershared-references/integration-contract.md§2 (Policy D1 — standalone/exa-searchhas no documented fallback, so unresolved helper terminates with an explicit error). - MAX_RESULTS = 10 — Default number of results to return.
Overrides (append to arguments):
/exa-search "RAG pipelines" — max: 5— top 5 results/exa-search "diffusion models" — category: research paper— research papers only/exa-search "startup funding" — category: news, start date: 2025-01-01— recent news/exa-search "transformer" — content: text, max chars: 8000— full text mode/exa-search "transformer" — content: summary— LLM-generated summaries/exa-search "transformer" — domains: arxiv.org,huggingface.co— domain filter/exa-search "https://arxiv.org/abs/2301.07041" — similar— find similar pages
Setup
Exa requires the exa-py SDK and an API key:
pip install exa-py
Set your API key:
export EXA_API_KEY=your-key-here
Get a key from exa.ai.
Workflow
Step 1: Parse Arguments
Parse $ARGUMENTS for:
- query: The search query (required) or a URL (for
find-similarmode) - similar: If present, use
find-similarmode instead of search - max: Override MAX_RESULTS
- category:
research paper,news,company,personal site,financial report,people - content:
highlights(default),text,summary,none - max chars: Max characters for content extraction
- type: Search type —
auto(default),neural,fast,instant - domains: Comma-separated include domains
- exclude domains: Comma-separated exclude domains
- include text: Phrase that must appear in results
- exclude text: Phrase to exclude from results
- start date: ISO 8601 date — only results after this
- end date: ISO 8601 date — only results before this
- location: Two-letter ISO country code
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.
- 12d ago First seen · 206 lines · 49 tokens per session scan A c9ffd1e95004
exa-search 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 49 tokens to every session and 2,387 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.
Other skills, from other repositories
figure-style
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots. Use for a figure that will ship in a report, paper, export, or kept artifact. Covers data fidelity, label economy, color threading, chart choice, layout, and render-then-verify QA without imposing a…
remote-compute-ssh
Evaluate and use SSH Remote Compute before choosing where to run GPU, high-memory, parallel, batch, model-inference, bioinformatics, or other long-running scientific work; supports short remote commands and asynchronous jobs with automatic harvest and analysis.
ligandmpnn
Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to design metal-coordinating sites where the geometry must be…
evo2
Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring…
memory
Cross-project research memory. Deep-dive past projects' notes, record corrections, and save cross-project insights across all Luxas research projects.
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…