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/paper-figureWrote 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/paper-figure)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-figure"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-figure/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/paper-figure"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-figure.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 | $0.00041 | $0.03848 |
| Opus 5.5 | $0.00016 | $0.01539 |
| Sonnet 5.5 | $0.00008 | $0.00770 |
| Haiku 4.5 | $0.00004 | $0.00385 |
Grade A, and why
paper-figure 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 7d 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
3 near-identical copies found in the catalogue:
- paper-figure — 100% identical, 21 lines differ
- paper-figure — 86% identical, 62 lines differ
- paper-figure — 84% identical, 59 lines differ
How it starts
The opening of the file, as written. The whole thing — 326 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Figure: Publication-Quality Plots from Experiment Data
Before each reviewer slot, follow Selectable Reviewers: resolve invocation →
.aris/reviewer.json→ Claude Code default (Codex MCP) withreviewer_route.pythrough the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only tobackend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections areREVIEW_UNAVAILABLE. Classify acceptance from actual families/completion/scope. Forward invocation reviewer/model/effort overrides to reviewer-bearing children. Generation workers are unchanged.
Generate all figures and tables for a paper based on: $ARGUMENTS
Scope: What This Skill Can and Cannot Do
| Category | Can auto-generate? | Examples |
|---|---|---|
| Data-driven plots | ✅ Yes | Line plots (training curves), bar charts (method comparison), scatter plots, heatmaps, box/violin plots |
| Comparison tables | ✅ Yes | LaTeX tables comparing prior bounds, method features, ablation results |
| Multi-panel figures | ✅ Yes | Subfigure grids combining multiple plots (e.g., 3×3 dataset × method) |
| Architecture/pipeline diagrams | ❌ No — manual | Model architecture, data flow diagrams, system overviews. At best can generate a rough TikZ skeleton, but expect to draw these yourself using tools like draw.io, Figma, or TikZ |
| Generated image grids | ❌ No — manual | Grids of generated samples (e.g., GAN/diffusion outputs). These come from running your model, not from this skill |
| Photographs / screenshots | ❌ No — manual | Real-world images, UI screenshots, qualitative examples |
In practice: For a typical ML paper, this skill handles ~60% of figures (all data plots + tables). The remaining ~40% (hero figure, architecture diagram, qualitative results) need to be created manually and placed in figures/ before running /paper-write. The skill will detect these as "existing figures" and preserve them.
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.
- 7d ago Changed · +13 lines 7d115c37bc7d
- 1mo ago Changed 1bcc797faf94
- 1mo ago First seen · 313 lines · 41 tokens per session scan A 4bb31eab22c7
paper-figure is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (18,117 stars, last pushed 3d ago), licensed MIT. It adds 41 tokens to every session and 3,848 once invoked, about $0.0002 per session on Opus 5.5. 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.
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