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/result-to-claimWrote 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/result-to-claim)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim/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/result-to-claim"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/result-to-claim.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00063 | $0.04305 |
| Opus 5 | $0.00032 | $0.02152 |
| Sonnet 5 | $0.00013 | $0.00861 |
| Haiku 4.5 | $0.00006 | $0.00430 |
Grade A, and why
result-to-claim 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 5d 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 — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Result-to-Claim Gate
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It is verdict-bearing — it judges whether results support a claim. Re-running that verdict on a wall-clock timer adds no new signal (the verdict changes only when the results change, not when the clock ticks). What you actually want to schedule is the external wait that precedes it — experiments done → then run this gate once. Seeshared-references/external-cadence.md.
Experiments produce numbers; this gate decides what those numbers mean. Collect results from available sources, get a Codex judgment, then auto-route based on the verdict.
Context: $ARGUMENTS
When to Use
- After a set of experiments completes (main results, not just sanity checks)
- Before committing to claims in a paper or review response
- When results are ambiguous and you need an objective second opinion
Workflow
Step 1: Collect Results
Gather experiment data from whatever sources are available in the project:
- W&B (preferred):
wandb.Api().run("<entity>/<project>/<run_id>").history()— metrics, training curves, comparisons - EXPERIMENT_LOG.md: full results table with baselines and verdicts
- EXPERIMENT_TRACKER.md: check which experiments are DONE vs still running
- Log files:
ssh server "tail -100 /path/to/training.log"if no other source idea-stage/docs/research_contract.md(legacy fallback:docs/research_contract.md): intended claims and experiment design
Assemble the key information:
- What experiments were run (method, dataset, config)
- Main metrics and baseline comparisons (deltas)
- The intended claim these experiments were designed to test
- Any known confounds or caveats
Step 1.5: Deterministic evidence pre-check (before spending a Codex call)
For every claim that cites a specific number + a source file, verify the evidence
exists mechanically — no model call — to catch hallucinated evidence before
the jury runs (see shared-references/evidence-precheck.md).
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.
- 5d ago Changed ba53865e333f
- 9d ago First seen · 312 lines · 63 tokens per session scan A 251940dc0a44
result-to-claim 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 63 tokens to every session and 4,305 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.
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