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 paper-poster-htmlgit 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/paper-poster-html)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-poster-html"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-poster-html/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-poster-html"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-poster-html.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00107 | $0.04682 |
| Opus 5 | $0.00053 | $0.02341 |
| Sonnet 5 | $0.00021 | $0.00936 |
| Haiku 4.5 | $0.00011 | $0.00468 |
Grade A, and why
paper-poster-html 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 4d 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 — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Poster (HTML): measurement-gated poster generation
One HTML file styled for an exact print canvas (@page { size: W H }), rendered to PDF
via Playwright print emulation. Iterate by measuring, not eyeballing — the screen
preview lies; only print emulation at the correct viewport tells the truth. Core gate
machinery is adapted from posterly (MIT, ©
2026 Ruishuo Chen — see NOTICE.md and LICENSES/posterly-MIT.txt); ARIS adds style
discipline gates, figure-provenance gates, the cross-model review loop, and the
anti-patch-loop fix vocabulary.
Why this skill exists (the failure it prevents)
A predecessor pipeline produced a poster with 30+ colors, zero real paper figures, a screen-pixel canvas, and tiny formulas floating in oversized boxes, then spent 12+ review rounds making it worse — each round added a new badge color or bespoke SVG patch. The cure is structural, not exhortative:
- Hard gates run before any aesthetic opinion (alignment, style, assets must PASS first — a reviewer never sees an unmeasured poster).
- A closed fix vocabulary — visual-review fixes can only touch design tokens, whole catalogued components, content rebalance, assets, or canvas choice. New inline styles / new hex values / bespoke decorations are structurally forbidden.
- Two-hue discipline as a machine check, not a style suggestion.
- Real paper figures with provenance manifest, or the gate fails.
Mental model
paper (.tex / PDF) ──► content plan + claim→evidence audit (codex, fresh)
│
figures extracted ─────────┤ FIGURE_MANIFEST.json (provenance, sha256)
(real paper figures ONLY) ▼
template scaffold ──► fill ──► run_gates.py ◄─── HARD, loop here
preflight → style → asset → measure → polish
│ all hard gates PASS
▼
Claude visual review (≤3 issues × ≤3 rounds, fix-vocabulary only)
│ score ≥ 9
▼
codex final cross-model review (fresh thread, full HTML+PDF)
│ pass
▼
verify-final → poster.pdf + GATE_REPORT.json
What ships with it
30 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- DESIGN_FINAL.md 9.7 KB
- IMPLEMENTATION_CONVENTIONS.md 8.7 KB
- LICENSES/posterly-MIT.txt 1.0 KB
- NOTICE.md 3.2 KB
- scripts/_posterly/__init__.py 0 B runs code
- scripts/_posterly/canvas.py 7.2 KB runs code
- scripts/_posterly/measure.py 24 KB runs code
- scripts/_posterly/polish.py 21 KB runs code
- scripts/_posterly/preflight.py 21 KB runs code
- scripts/_posterly/render.py 8.0 KB runs code
- scripts/_posterly/textutil.py 616 B runs code
- scripts/_posterly/verify_final.py 5.5 KB runs code
- scripts/asset_check.py 36 KB runs code
- scripts/extract_pdf_figures.py 25 KB runs code
- scripts/poster_check.py 9.9 KB runs code
- scripts/preprocess_figures.py 9.1 KB runs code
- scripts/render_preview.py 7.5 KB runs code
- scripts/run_gates.py 20 KB runs code
- scripts/style_check.py 52 KB runs code
- templates/COMPONENTS.md 31 KB
- templates/landscape_4col.html 41 KB
- templates/landscape_hero.html 44 KB
- templates/portrait_2col.html 39 KB
- templates/README.md 10 KB
- templates/tokens/acl.json 431 B
- templates/tokens/cvpr.json 432 B
- templates/tokens/generic.json 435 B
- templates/tokens/iclr.json 432 B
- templates/tokens/icml.json 432 B
- templates/tokens/neurips.json 435 B
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.
- 4d ago Changed b3c55d212f5c
- 11d ago First seen · 324 lines · 107 tokens per session scan A 6c44eabe999a
paper-poster-html 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 107 tokens to every session and 4,682 once invoked, about $0.0005 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-designer
Advises on the design of the three core figures in a technical paper: the Motivated Example (Figure 1), the Solution Overview (Methodology), and the Experimental Results figures. Recommends the right design paradigm, layout, labelling, and tool for each figure type, then runs a quality-control audit. Use when the user…
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.
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.
openfold3
Structure prediction using OpenFold3, an open-weights PyTorch reproduction of AlphaFold3 from the AlQuraishi Lab. Use this skill when predicting protein/nucleic-acid/ligand complex structures with an Apache-2.0-licensed AF3 reimplementation.
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…
alphafold2
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency…