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/auto-paper-improvement-loopWrote 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/auto-paper-improvement-loop)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/auto-paper-improvement-loop"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/auto-paper-improvement-loop/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/auto-paper-improvement-loop"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/auto-paper-improvement-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Agent Snooping · line 474 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Excessive Agency · line 669 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 | $0.00067 | $0.10377 |
| Opus 5.5 | $0.00027 | $0.04151 |
| Sonnet 5.5 | $0.00013 | $0.02075 |
| Haiku 4.5 | $0.00007 | $0.01038 |
Grade A, and why
auto-paper-improvement-loop 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 8d 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:
- auto-paper-improvement-loop — 95% identical, 17 lines differ
- auto-paper-improvement-loop — 91% identical, 130 lines differ
How it starts
The opening of the file, as written. The whole thing — 711 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Paper Improvement Loop: Review → Fix → Recompile
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.
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It already loops internally (review → fix → recompile) with its own round structure and a deliberate fresh-reviewer bias guard each round (nocodex-reply). Re-asking it to "improve the paper" on a wall-clock timer produces no new signal — quality changes when the review changes, not when the clock ticks — and a timed re-run that also accepts its own output to decide when to stop crosses into self-acquittal (acceptance-gate.md). Schedule the external wait that precedes it, not the improvement loop. Seeshared-references/external-cadence.md.
Autonomously improve the paper at: $ARGUMENTS
Context
This skill is designed to run after Workflow 3 (/paper-plan → /paper-figure → /paper-write → /paper-compile). It takes a compiled paper and iteratively improves it through external LLM review.
Unlike /auto-review-loop (which iterates on research — running experiments, collecting data, rewriting narrative), this skill iterates on paper writing quality — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.
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.
- 8d ago Changed · +15 lines 022629e450b9
- 29d ago Changed e01793536257
- 1mo ago Changed · -2 tokens per session 6472a47dae81
- 1mo ago Changed · +22 lines cbc81c8c8049
- 1mo ago First seen · 674 lines · 69 tokens per session scan A acc1114d7f6f
auto-paper-improvement-loop is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (18,176 stars, last pushed 3d ago), licensed MIT. It adds 67 tokens to every session and 10,377 once invoked, about $0.0003 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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