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-talkWrote 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-talk)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-talk"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-talk/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-talk"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-talk.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00113 | $0.05268 |
| Opus 5 | $0.00056 | $0.02634 |
| Sonnet 5 | $0.00023 | $0.01054 |
| Haiku 4.5 | $0.00011 | $0.00527 |
Grade A, and why
paper-talk 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 13d 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:
- paper-talk — 98% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Talk: End-to-End Conference Talk Pipeline
Workflow: from a completed paper to a conference-ready talk artifact — slide outline, Beamer source, editable PPTX, speaker notes, full talk script, polished visuals, and assurance checks.
Pipeline target: $ARGUMENTS
Assurance Ladder
The skill takes an explicit — assurance: argument; default is polished.
| Level | Phases run | Use when |
|---|---|---|
draft |
0 → 1 → 2 → 5 → 6 | Internal practice talk; quick first-pass deck |
polished (default) |
0 → 1 → 2 → 3 → 5 → 6 | Lab seminar, workshop, default conference talk |
conference-ready (or submission) |
0 → 1 → 2 → 3 → 4 → 5 → 6 | Top-venue oral / spotlight where slide claims are scrutinised; or anonymous submission |
Phase 4 (assurance checks) is opt-in via — assurance: conference-ready.
The lower levels skip claim / citation / anonymity audits, which is correct
when content has already been audited at the paper-writing stage.
Hard Invariants
These are non-negotiable across all phases:
- Original paper is read-only. This workflow consumes the paper directory; it never modifies
paper/main.texor other paper artifacts. - Original deck is preserved.
/paper-slidesproduces a baseline deck;/slides-polishwrites a_polishedversioned copy. The original Beamer + PPTX are never overwritten. - Speaker notes are byte-stable. Polish must not change
slide.notes_slidecontent. Phase 4 verifies this. - No new content anywhere in the pipeline. All slide text, speaker notes, talk script, Q&A answers, claims, numbers, citations, URLs, author names, affiliations, anonymity placeholders, and experiment results must be either paper-grounded (extracted from
PAPER_DIR/artefacts) or explicitly user-provided. The pipeline never invents content during outline, build, polish, audit, or export. Phase-4 anonymity scan + claim audit verify this end-to-end. - No slide reordering. Add / drop / reorder requires explicit user flags.
- Cross-model independence. Per-page Codex calls in
/slides-polishuse fresh threads (nocodex-reply). See../shared-references/reviewer-independence.md. - Anonymity fail-closed. If any audit (or any Codex fix proposal) would replace a placeholder with a real title / count / URL, the workflow halts and surfaces the proposal for human review. See
../shared-references/experiment-integrity.md. - Style references are guidance, not text source. A
— reference:PDF or— style:preset informs visual weight and structural rhythm; never copy prose, examples, slide titles, or speaker-note text from the reference. - Final report cannot be
conference-readyunless required audits pass. Phase 6 verifies and downgrades verdict if audits fail. reasoning_effort: xhighis invariant across alleffortlevels for any Codex call invoked by sub-skills.
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.
- 13d ago First seen · 382 lines · 113 tokens per session scan A 0bd7722963e9
paper-talk 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 113 tokens to every session and 5,268 once invoked, about $0.0006 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
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.
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…
evo2
Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring…
ligandmpnn
Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to design metal-coordinating sites where the geometry must be…
memory
Cross-project research memory. Deep-dive past projects' notes, record corrections, and save cross-project insights across all Luxas research projects.
ml-training-recipes
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning…