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/semantic-scholarWrote 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/semantic-scholar)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/semantic-scholar"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/semantic-scholar/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/semantic-scholar"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/semantic-scholar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Data Exfiltration · line 101 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00080 | $0.03127 |
| Opus 5 | $0.00040 | $0.01563 |
| Sonnet 5 | $0.00016 | $0.00625 |
| Haiku 4.5 | $0.00008 | $0.00313 |
Grade A, and why
semantic-scholar scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
If `$S2_FETCHER` is empty (Policy D1 cascade), fall back to inline Python using `urllib` against `https://api.semanticscholar.org/graph/v1/paper/search`. How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Scholar Paper Search
Search topic or paper ID: $ARGUMENTS
Role & Positioning
This skill is the published venue counterpart to /arxiv:
| Skill | Source | Best for |
|---|---|---|
/arxiv |
arXiv API | Latest preprints, cutting-edge unrefereed work |
/semantic-scholar |
Semantic Scholar API | Published journal/conference papers (IEEE, ACM, Springer, etc.) with citation counts, venue info, TLDR |
Do NOT duplicate arXiv's job. If results contain an externalIds.ArXiv field, the paper is also on arXiv — note this but do not re-fetch from arXiv.
Constants
- MAX_RESULTS = 10 — Default number of search results.
- S2_FETCHER — canonical name
semantic_scholar_fetch.py, resolved pershared-references/integration-contract.md§2 (Policy D1 — primary + fallback cascade). If unresolved (canonical chain exhausted), fall back to the inline Python alternative documented in Step 2. - DEFAULT_FILTERS — For general research queries, apply these by default to reduce noise:
--fields-of-study "Computer Science,Engineering"--publication-types JournalArticle,Conference
Overrides (append to arguments):
/semantic-scholar "topic" - max: 20— return up to 20 results/semantic-scholar "topic" - type: journal— only journal articles/semantic-scholar "topic" - type: conference— only conference papers/semantic-scholar "topic" - min-citations: 50— only highly-cited papers/semantic-scholar "topic" - year: 2022-— papers from 2022 onward/semantic-scholar "topic" - fields: all— remove default field-of-study filter/semantic-scholar "topic" - sort: citations— bulk search sorted by citation count/semantic-scholar "DOI:10.1109/..."— fetch a single paper by DOI
Workflow
Step 1: Parse Arguments
Parse $ARGUMENTS for directives:
- Query or ID: main search term, or a paper identifier:
- DOI:
10.1109/TWC.2024.1234567 - Semantic Scholar ID:
f9314fd99be5f2b1b3efcfab87197d578160d553 - ArXiv:
ARXIV:2006.10685 - Corpus:
CorpusId:219792180
- DOI:
- max: N: override MAX_RESULTS- type: journal|conference|review|all: map to--publication-types- min-citations: N: map to--min-citations- year: RANGE: map to--year(e.g.2022-,2020-2024)- fields: FIELDS: override--fields-of-study(useallto remove filter)- sort: citations|date: usesearch-bulkwith--sort citationCount:descorpublicationDate:desc
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.
- 8d ago First seen · 237 lines · 80 tokens per session scan A 41be9cd7d08d
semantic-scholar 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 80 tokens to every session and 3,127 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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