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
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill research-wikigit clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleepWrote 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/research-wiki)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-wiki"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-wiki/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/research-wiki"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-wiki.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 | $0.00073 | $0.05397 |
| Opus 5 | $0.00036 | $0.02698 |
| Sonnet 5 | $0.00015 | $0.01079 |
| Haiku 4.5 | $0.00007 | $0.00540 |
Grade A, and why
research-wiki 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 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.
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:
- research-wiki — 88% identical, 116 lines differ
How it starts
The opening of the file, as written. The whole thing — 462 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Wiki: Persistent Research Knowledge Base
Subcommand: $ARGUMENTS
Overview
The research wiki is a persistent, per-project knowledge base that accumulates structured knowledge across the entire ARIS research lifecycle. Unlike one-off literature surveys that are used and forgotten, the wiki compounds — every paper read, idea tested, experiment run, and review received makes the wiki smarter.
Inspired by Karpathy's LLM Wiki pattern: compile knowledge once, keep it current, don't re-derive on every query.
Core Concepts
Four Entity Types
| Entity | Directory | Node ID format | What it represents |
|---|---|---|---|
| Paper | papers/ |
paper:<slug> |
A published or preprint research paper |
| Idea | ideas/ |
idea:<id> |
A research idea (proposed, tested, or failed) |
| Experiment | experiments/ |
exp:<id> |
A concrete experiment run with results |
| Claim | claims/ |
claim:<id> |
A theorem/headline with an honest PROOF status — born via /proof-checker (see Hook 4) |
Typed Relationships (graph/edges.jsonl)
| Edge type | From → To | Meaning |
|---|---|---|
extends |
paper → paper | Builds on prior work |
contradicts |
paper → paper | Disagrees with results/claims |
addresses_gap |
paper|idea → gap | Targets a known field gap |
inspired_by |
idea → paper | Idea sourced from this paper |
tested_by |
idea|claim → exp | Tested in this experiment |
supports |
exp → claim|idea | Experiment confirms claim |
invalidates |
exp → claim|idea | Experiment disproves claim |
supersedes |
paper → paper | Newer work replaces older |
Edges are stored in graph/edges.jsonl only. The ## Connections section on each page is auto-generated from the graph — never hand-edit it.
Capture hygiene (anti-self-poisoning)
Before persisting an idea / claim / experiment note, screen it for
operational noise that would harden into a self-cited falsehood (see
shared-references/capture-antipatterns.md).
Resolve the helper via the canonical chain (integration-contract §2):
.aris/tools/capture_filter.py → tools/capture_filter.py →
$ARIS_REPO/tools/capture_filter.py (warn-and-skip if unresolved). Run
python3 <capture_filter> - on the note text; if it flags env-failure /
transient-error / negative-tool-claim, do NOT store it as a durable node —
rewrite it to the fix / missing config / workaround, or drop it. Never store
"codex/gemini/the reviewer can't do X" — that gets loaded into every future
session and cited against the agent long after the real cause is gone. (The wiki's
"failed ideas → anti-repeat memory" is the GOOD inverse: a class-level research
finding, not operational noise.)
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 · 462 lines · 73 tokens per session scan A 3bd314f83f2d
research-wiki 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 73 tokens to every session and 5,397 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
memory
Cross-project research memory. Deep-dive past projects' notes, record corrections, and save cross-project insights across all Luxas research projects.
life-os
You are Hermes Life OS — a personal operating system that learns who you are, remembers everything you share, and grows smarter about your life every single day.
new
Start a new session — save summary to memory, then mark as reset so next message gets a fresh greeting. OpenClaw-compatible. Triggers on /new, /reset, /agent:new, "nueva sesión", "new session", "reset".
compact
Flush important session context to daily log (manual memory flush). Does NOT invoke native /compact. Triggers on /compact, /agent:compact, /flush, "guarda memoria", "flush".
session_logs
Search and analyse your own conversation history from session files. Use when: user references older conversations, asks what was said before, or needs historical context from past sessions. NOT for: current session messages (already in context), non-session files, or external logs.
onboard
One-time researcher onboarding interview that compiles a taste/ corpus (SOUL.md, STYLE.md, METHODOLOGY.md, DISMISSALS.md, MEMORY.md, INDEX.md) capturing the researcher's intellectual taste, beliefs, and values. Auto-injects INDEX.md into every future session via SessionStart. Triggers on first install ("no taste…