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/wiki-enrichWrote 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/wiki-enrich)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich/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/wiki-enrich"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/wiki-enrich.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00087 | $0.04435 |
| Opus 5 | $0.00044 | $0.02218 |
| Sonnet 5 | $0.00017 | $0.00887 |
| Haiku 4.5 | $0.00009 | $0.00443 |
Grade A, and why
wiki-enrich 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 9d 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.
| 4 | **arXiv API abstract — fresh fetch** (`--source arxiv` to pin) | `curl http://export.arxiv.org/api/query?id_list=<arxiv_id>` — log label: `arxiv-api-abstract` | How it starts
The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wiki Enrich: Fill Paper TODO Sections (Karpathy LLM-Wiki)
Target: $ARGUMENTS
Why this skill exists
ingest_paper (called by /research-lit, /arxiv, /alphaxiv, /deepxiv, /semantic-scholar, /exa-search) only renders the per-paper scaffold — frontmatter + abstract + 10 fillable _TODO._ placeholder sections (plus two protected sections: ## Connections is graph-summary and ## Abstract (original) is auto-populated when --arxiv-id is given). No downstream skill in ARIS fills those 10 sections; the wiki sits as TODO until someone reads each paper.
This contradicts the Karpathy LLM-wiki design (https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f):
"You never (or rarely) write the wiki yourself — the LLM writes and maintains all of it. … The tedious part of maintaining a knowledge base is not the reading or the thinking — it's the bookkeeping. … LLMs don't get bored, don't forget to update a cross-reference, and can touch 15 files in one pass."
/wiki-enrich is the missing back half of ingest_paper: it reads each scaffolded paper page, fetches paper content from external sources via a graceful fallback chain (see Phase 2.3 for the full 5-source chain), and rewrites the 10 fillable TODO sections into 1-3 sentence prose summaries.
Constants
- WIKI_ROOT =
research-wiki/— Resolved relative to git root. Skill hard-fails if not a directory. - TARGET_DEFAULT =
missing— When no target is given, enrich only papers with ≥1 TODO section. Other targets:<slug>(one paper) orall(every paper, even ones already enriched — usually combined with--forceto overwrite). - SOURCE_DEFAULT =
auto— Fetch order: alphaxiv overview → alphaxiv abs → deepxiv brief → arXiv API abstract → page abstract fallback. First non-empty wins (full chain documented in Phase 2.3 table). Override with--sourceto pin one source. - MAX_PAPERS = 20 — Hard cap per invocation; LLMs touch many files but token budgets are real. Override with
--max N. - FORCE = false — When
false(default), skip sections that already have non-TODO content. Whentrue, overwrite every fillable section, but never touch the two protected sections:## Connections(auto-generated fromedges.jsonl) and## Abstract (original)(immutable arXiv-fetched source data). - SECTIONS_TO_FILL — 10 fillable sections + 2 protected.
ingest_paper(research_wiki.py:436-473) scaffolds 11 section headers unconditionally and a 12th —## Abstract (original)— only when arXiv returns an abstract for the given--arxiv-id(research_wiki.py:469-473). Of these, 10 carry a_TODO._(or_TODO: fill in after reading._) marker and need filling. The other 2 —## Connections(position 10 in the enumeration below) and## Abstract (original)(position 12, conditional) — are protected by construction:Connectionsis auto-generated fromgraph/edges.jsonl,Abstract (original)is immutable source data from the arXiv API. This skill writes to the 10, never the 2.One-line thesis(marker:_TODO: fill in after reading._)Problem / Gap(marker:_TODO._)Method(marker:_TODO._)Key Results(marker:_TODO._)Assumptions(marker:_TODO._)Limitations / Failure Modes(marker:_TODO._)Reusable Ingredients(marker:_TODO._)Open Questions(marker:_TODO._)Claims(marker:_TODO._) — fill with_No claims tracked yet._if noclaim:edges point to this paper; otherwise list them.Connections— NEVER edit (auto-generated fromgraph/edges.jsonl).Relevance to This Project(marker:_TODO._) — useRESEARCH_BRIEF.md,AGENTS.md(or legacyCLAUDE.md), orgap_map.mdfor project context. If no project context exists, leave as TODO and report it.Abstract (original)— leave alone (already populated byingest_paperwhen--arxiv-idwas used).
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.
- 9d ago First seen · 256 lines · 87 tokens per session scan A 3c3748a4da25
wiki-enrich 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 87 tokens to every session and 4,435 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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