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 training-checkgit 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/training-check)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/training-check"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/training-check/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/training-check"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/training-check.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 | $0.00035 | $0.00986 |
| Opus 5 | $0.00017 | $0.00493 |
| Sonnet 5 | $0.00007 | $0.00197 |
| Haiku 4.5 | $0.00003 | $0.00099 |
Grade A, and why
training-check 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 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.
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:
- training-check — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Training Check
You are now in interactive watch / 交互式训练监控模式.
Keep the current session open and report directly in the current terminal. The user is watching this terminal for updates. By default, run a training health check every 30 minutes, output a concise but complete analysis report after each check, state the next check time, then continue monitoring.
This skill checks training quality, not basic process health. Process health checks such as whether a tmux session exists or whether the GPU is idle can be handled by watchdog-style tooling; this skill focuses on whether the run is still worth continuing.
Inputs To Establish First
Before the first check, identify or ask for the minimum monitoring context:
- WandB run path or URL, if available.
- Fallback log path, SSH command, or local command for reading recent training logs.
- Training target, expected baseline, and key metrics that define success.
- How the training was launched, so it can be stopped if needed.
- Project notes path for recording decisions and evidence.
If a source is unavailable, say so clearly and continue with the available source. If both WandB and fallback logs are unreachable, report the connectivity issue, classify the round as WAIT, and check again later. Do not infer that training is bad only because data is unreachable.
Per-Round Check
Every round, read WandB first when configured. If WandB is unreachable, read the fallback logs. Inspect at least:
- Training loss trend over recent checkpoints or steps.
- Eval metrics and whether they improve, flatten, or degrade against baseline.
- NaN or Inf in loss, gradients, activations, or logged metrics.
- Sudden loss spikes, divergence, or repeated failed evaluations.
- Learning rate schedule behavior.
- Gradient norm, if logged.
- Plateau patterns that suggest the run is no longer useful.
Output one report in the current terminal with this structure:
## Training Check - <local timestamp>
- Data source: wandb_ok | log_fallback | unreachable
- Run: <wandb run or training identifier>
- Recent metrics: <loss/eval/lr/grad summary>
- Anomalies: <NaN/Inf/spike/divergence/plateau findings>
- Evidence: <WandB URL, log lines, metric values, or files inspected>
- Decision: CONTINUE | WAIT | STOP
- Reason: <why this decision is justified>
- Next check: <local timestamp, normally 30 minutes later unless ending>
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 · 84 lines · 35 tokens per session scan A c48dc4b1e5bc
training-check is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (15,970 stars, last pushed 2d ago), licensed MIT. It adds 35 tokens to every session and 986 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 2026-09-03.
Other skills, from other repositories
esmfold2
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the ESMC-{300M,600M,6B} protein language models from the same release…
scgpt
Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For…
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…
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…
experiment-tracking-swanlab
Provides guidance for experiment tracking with SwanLab. Use when you need open-source run tracking, local or self-hosted dashboards, and lightweight media logging for ML workflows.
analyze_current
Read and understand the current baseline implementation. Extract all relevant information about the existing approach without modifying anything, and record the analysis as a structured JSON entry.