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/research-refine-pipelineWrote 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-refine-pipeline)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-refine-pipeline"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-refine-pipeline/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-refine-pipeline"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/research-refine-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 168 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.01481 |
| Opus 5 | $0.00036 | $0.00740 |
| Sonnet 5 | $0.00015 | $0.00296 |
| Haiku 4.5 | $0.00007 | $0.00148 |
Grade A, and why
research-refine-pipeline 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-refine-pipeline — 97% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Refine Pipeline: End-to-End Method and Experiment Planning
Refine and concretize: $ARGUMENTS
Overview
Use this skill when the user does not want to stop at a refined method. The goal is to produce a coherent package that includes:
- a problem-anchored, elegant final proposal
- the review history explaining why the method is focused
- a detailed experiment roadmap tied to the paper's claims
- a compact pipeline summary that says what to run next
This skill composes two existing workflows:
research-refinefor method refinementexperiment-planfor claim-driven validation planning
For stage-specific detail, read these sibling skills only when needed:
../research-refine/SKILL.md../experiment-plan/SKILL.md
Core Rule
Do not plan a large experiment suite on top of an unstable method. First stabilize the thesis. Then turn the stable thesis into experiments.
Default Outputs
refine-logs/FINAL_PROPOSAL.mdrefine-logs/REVIEW_SUMMARY.mdrefine-logs/REFINEMENT_REPORT.mdrefine-logs/EXPERIMENT_PLAN.mdrefine-logs/EXPERIMENT_TRACKER.mdrefine-logs/PIPELINE_SUMMARY.md
Workflow
Phase 0: Triage the Starting Point
- Extract the problem, rough approach, constraints, resources, and target venue.
- Check whether
refine-logs/FINAL_PROPOSAL.mdalready exists and still matches the current request. - If the proposal is missing, stale, or materially different from the current request, run the full
research-refinestage. - If the proposal is already strong and aligned, reuse it and jump to experiment planning.
- If in doubt, prefer re-running
research-refinerather than planning experiments for the wrong method.
Phase 1: Method Refinement Stage
Run the research-refine workflow and keep its V3 philosophy intact:
- preserve the Problem Anchor
- prefer the smallest adequate mechanism
- keep one dominant contribution
- modernize only when it improves the paper
Exit this stage only when these are explicit:
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 · 187 lines · 73 tokens per session scan A d5091663645d
research-refine-pipeline 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 1,481 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.
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