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/experiment-planWrote 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/experiment-plan)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/experiment-plan"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/experiment-plan/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/experiment-plan"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/experiment-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk fail
- 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 230 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 | $0.00080 | $0.02209 |
| Opus 5 | $0.00040 | $0.01104 |
| Sonnet 5 | $0.00016 | $0.00442 |
| Haiku 4.5 | $0.00008 | $0.00221 |
Grade A, and why
experiment-plan 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 11d 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
2 near-identical copies found in the catalogue:
- experiment-plan — 100% identical, 2 lines differ
- experiment-plan — 91% identical, 11 lines differ
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Plan: Claim-Driven, Paper-Oriented Validation
Refine and concretize: $ARGUMENTS
Overview
Use this skill after the method is stable enough that the next question becomes: what exact experiments should we run, in what order, to defend the paper? If the user wants the full chain in one request, prefer /research-refine-pipeline.
The goal is not to generate a giant benchmark wishlist. The goal is to turn a proposal into a claim -> evidence -> run order roadmap that supports four things:
- the method actually solves the anchored problem
- the dominant contribution is real and focused
- the method is elegant enough that extra complexity is unnecessary
- any frontier-model-era component is genuinely useful, not decorative
Constants
- OUTPUT_DIR =
refine-logs/— Default destination for experiment planning artifacts. - MAX_PRIMARY_CLAIMS = 2 — Prefer one dominant claim plus one supporting claim.
- MAX_CORE_BLOCKS = 5 — Keep the must-run experimental story compact.
- MAX_BASELINE_FAMILIES = 3 — Prefer a few strong baselines over many weak ones.
- DEFAULT_SEEDS = 3 — Use 3 seeds when stochastic variance matters and budget allows.
Workflow
Phase 0: Load the Proposal Context
Read the most relevant existing files first if they exist:
refine-logs/FINAL_PROPOSAL.mdrefine-logs/REVIEW_SUMMARY.mdrefine-logs/REFINEMENT_REPORT.md
Extract:
- Problem Anchor
- Dominant contribution
- Optional supporting contribution
- Critical reviewer concerns
- Data / compute / timeline constraints
- Which frontier primitive is central, if any
If these files do not exist, derive the same information from the user's prompt.
Phase 1: Freeze the Paper Claims
Before proposing experiments, write down the claims that must be defended.
Use this structure:
- Primary claim: the main mechanism-level contribution
- Supporting claim: optional, only if it directly strengthens the main paper story
- Anti-claim to rule out: e.g. "the gain only comes from more parameters," "the gain only comes from a larger search space," or "the modern component is just decoration"
- Minimum convincing evidence: what would make each claim believable to a strong reviewer?
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.
- 11d ago First seen · 250 lines · 80 tokens per session scan A c5b53692ff95
experiment-plan 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 80 tokens to every session and 2,209 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 2026-08-30.
Other skills, from other repositories
figure-style
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots. Use for a figure that will ship in a report, paper, export, or kept artifact. Covers data fidelity, label economy, color threading, chart choice, layout, and render-then-verify QA without imposing a…
remote-compute-ssh
Evaluate and use SSH Remote Compute before choosing where to run GPU, high-memory, parallel, batch, model-inference, bioinformatics, or other long-running scientific work; supports short remote commands and asynchronous jobs with automatic harvest and analysis.
paper-narrative
Judge and reshape the story told by an entire paper figure deck. Use when writing or revising a paper to derive a grounded brief from the manuscript and captions, review the full deck as a handling editor, and hand an ordered figure arc to figure-composer.
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…
literature-review
Find, verify, and synthesize scientific literature — from "what's the seminal paper for X" through full multi-source reviews. Covers grounding claims in real retrieved sources, avoiding fabricated citations, handling retractions, and calibrating confidence to evidence strength.
scvi-tools
Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score…