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-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/paper-plan)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-plan"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-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/paper-plan"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/paper-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- 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 366 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 | $0.00047 | $0.05184 |
| Opus 5 | $0.00023 | $0.02592 |
| Sonnet 5 | $0.00009 | $0.01037 |
| Haiku 4.5 | $0.00005 | $0.00518 |
Grade A, and why
paper-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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- paper-plan — 91% identical, 91 lines differ
How it starts
The opening of the file, as written. The whole thing — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Plan: From Review Conclusions to Paper Outline
Generate a structured, section-by-section paper outline from: $ARGUMENTS
Constants
- REVIEWER_MODEL =
gpt-6-astra— Model used via Codex MCP for outline review. Must be an OpenAI model. - TARGET_VENUE =
ICLR— Default venue. User can override (e.g.,/paper-plan "topic" — venue: NeurIPS). Supported:ICLR,NeurIPS,ICML,CVPR,ACL,AAAI,ACM,IEEE_JOURNAL(IEEE Transactions / Letters),IEEE_CONF(IEEE conferences). - MAX_PAGES — Page limit. For ML conferences: main body to Conclusion end (excluding references, appendix). ICLR=9, NeurIPS=9, ICML=8, AAAI=7 technical-content pages plus references unless the current AAAI CFP says otherwise. For IEEE venues: references ARE included in page count. IEEE journal Transactions ≈ 12-14 pages total, Letters ≈ 4-5 pages total; IEEE conference ≈ 5-8 pages total (including references).
Inputs
The skill expects one or more of these in the project directory:
- NARRATIVE_REPORT.md or STORY.md — research narrative with claims and evidence
- review-stage/AUTO_REVIEW.md — auto-review loop conclusions (fall back to
./AUTO_REVIEW.mdif not found) - Experiment results — JSON files in
figures/, screen logs, tables - idea-stage/IDEA_REPORT.md — from idea-discovery pipeline (if applicable) (fall back to
./IDEA_REPORT.mdif not found) - Compact files (if available):
idea-stage/IDEA_CANDIDATES.md(fall back to./IDEA_CANDIDATES.mdif not found),findings.md,EXPERIMENT_LOG.md— preferred over full files when present, saves context window
If none exist, ask the user to describe the paper's contribution in 3-5 sentences.
Orchestra-Guided Writing Overlay
Keep the existing insleep workflow and outputs, but use the shared references below to improve the quality of the story and outline.
- Read
../shared-references/writing-principles.mdwhen framing the one-sentence contribution, Abstract, Introduction, Related Work, or hero figure. - Read
../shared-references/venue-checklists.mdbefore freezing the outline for a specific venue. - Only load these references when needed; do not paste their full contents into the working draft.
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 755e0f06c488
- 12d ago First seen · 387 lines · 47 tokens per session scan A 32316a6e8009
paper-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 47 tokens to every session and 5,184 once invoked, about $0.0002 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.
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