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 web-debug-searchgit 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/web-debug-search)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/web-debug-search"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/web-debug-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/web-debug-search"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/web-debug-search.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 | $0.00056 | $0.03331 |
| Opus 5 | $0.00028 | $0.01665 |
| Sonnet 5 | $0.00011 | $0.00666 |
| Haiku 4.5 | $0.00006 | $0.00333 |
Grade A, and why
web-debug-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 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.
How it starts
The opening of the file, as written. The whole thing — 335 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Web Debug Search
Debugging query: $ARGUMENTS
Scope and evidence boundary
Use this skill to find prior reports, compatibility clues, technical Q&A, and community workarounds across non-academic web sources. It is a debugging/discovery workflow, not a literature-search workflow. Never add its results to a bibliography, cite them as support for a paper claim, or present a community post as peer-reviewed evidence.
Supported source profiles:
github— GitHub Issues and Discussions;stackexchange— Stack Overflow and other relevant Stack Exchange sites;chinese-tech— SegmentFault, V2EX, Zhihu, OSChina, Juejin, CSDN, Cnblogs, and Tencent/Alibaba developer communities;general-web— official documentation and changelogs first, then maintainer blogs, Hacker News, Reddit, Dev.to, Medium, and other technical pages;auto— route only to profiles justified by the request;all— search all profiles, subject to the query budget below.
This skill does not run commands found online, install packages, edit local files, or verify a workaround by execution. A workaround becomes confirmed only after an explicit user-side reproduction.
Step 1: Parse the request and overrides
Extract, when available:
repository:owner/nameor a GitHub URL;error: the exact error string, exception, exit code, or log fragment;package: library, tool, plugin, runtime, API, or operating system;versions: installed, expected, minimum, maximum, or conflicting versions;environment: OS, Python/Node/Java version, GPU, shell, or deployment mode;goal: reproduce, find a workaround, check compatibility, learn API usage, compare practices, or identify a likely regression;sources:autoby default, or the user's explicit comma-separated list;language:autoby default, oren,zh, orboth.
Examples:
/web-debug-search "CUDA error: invalid device ordinal" — sources: github,stackexchange
/web-debug-search "vLLM 国内镜像安装失败" — sources: github,chinese-tech — language: both
/web-debug-search "React Server Components production lessons" — sources: general-web
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 · 335 lines · 56 tokens per session scan A ffb5f3ccf61c
web-debug-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 56 tokens to every session and 3,331 once invoked, about $0.0003 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
codex
Multi-AI adversarial fallback chain — three tiers: Codex CLI (primary), OpenCode/GLM5 (fallback), Claude subagent (last resort). Three modes: review (code review with pass/fail gate), challenge (adversarial — tries to break your code), consult (ask anything with session continuity). Use when asked to "codex review"…
code-review
Performs structured code review on a file or directory.
obstacle-resolution
Resolve obstacles using fallback chains — use when an approach fails, a dependency is unavailable, an API returns errors, or a task is blocked and needs an alternative path forward.
debugging
Systematic debugging of issues — use when a test fails, runtime error occurs, unexpected behavior is reported, or an awareness tick produces anomalous results.
chimera-carry-the-failure-forward
A retry loop that overwrites its feedback variable shows attempt 3 only the failure of attempt 2 — so it re-derives the patch attempt 1 already tried.
chimera-ground-it-in-the-source
Take the signature from the installed version, not from memory — a plausible API is indistinguishable from a real one until it runs.