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 rebuttalgit 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/rebuttal)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/rebuttal"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/rebuttal/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/rebuttal"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/rebuttal.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00073 | $0.05863 |
| Opus 5 | $0.00036 | $0.02932 |
| Sonnet 5 | $0.00015 | $0.01173 |
| Haiku 4.5 | $0.00007 | $0.00586 |
Grade A, and why
rebuttal 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 5d 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 — 377 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow 4: Rebuttal
Prepare and maintain a grounded, venue-compliant rebuttal for: $ARGUMENTS
Scope
This skill is optimized for:
- text-only rebuttal under strict character/word limits (e.g. ICML single-document)
- per-reviewer thread responses where each reviewer renders independently (e.g. OpenReview-style)
- multiple reviewers with shared and reviewer-specific concerns
- follow-up rounds after the initial rebuttal
- safe drafting with no fabrication, no overpromise, and full issue coverage
This skill does not:
- run new experiments automatically
- generate new theorem claims automatically
- edit or upload a revised PDF
- submit to OpenReview / CMT / HotCRP
If the user already has new results, derivations, or approved commitments, the skill can incorporate them as user-confirmed evidence.
Lifecycle Position
Workflow 1: idea-discovery
Workflow 1.5: experiment-bridge
Workflow 2: auto-review-loop (pre-submission)
Workflow 3: paper-writing
Workflow 4: rebuttal (post-submission external reviews)
Constants
- VENUE =
ICML— Default venue. Override if needed. - RESPONSE_MODE =
TEXT_ONLY— v1 default. - REVIEWER_MODEL =
gpt-6-astra— Default model for the Codex backend. Used for internal stress-testing. Manual backend uses a model the user chooses — it must be a recognized model from a different family (OpenAI, Anthropic, Google, DeepSeek, Moonshot/Kimi, Qwen). - REVIEWER_BACKEND =
codex— Default: Codex MCP (xhigh). Override with— reviewer: oracle-profor Oracle MCP, or— reviewer: manualfor Manual Review MCP. If manual-review MCP is unavailable, stop and print the install command; do not fall back to Codex. Seeshared-references/reviewer-routing.md. - MAX_INTERNAL_DRAFT_ROUNDS = 2 — draft → lint → revise.
- VENUE_MODE =
single_document—single_documentfor one shared author response, orper_reviewer_threadwhen each reviewer thread renders independently. Confirm the venue/interface before drafting if unclear. Affects Phase 4/7 output shape. - STRESS_TEST_ROUNDS_BASE = 1 — One external reviewer critique round on the full response set. Add focused rounds for
reviewer_priority: pivotalresponses, terminating when the reviewer returns no new substantive issues. Hard cap at 5. - MAX_FOLLOWUP_ROUNDS = 3 — per reviewer thread.
- AUTO_EXPERIMENT = false — When
true, automatically invoke/experiment-bridgeto run supplementary experiments when the strategy plan identifies reviewer concerns that require new empirical evidence. Whenfalse(default), pause and present the evidence gap to the user for manual handling. - QUICK_MODE = false — When
true, only run Phase 0-3 (parse reviews, atomize concerns, build strategy). OutputsISSUE_BOARD.md+STRATEGY_PLAN.mdand stops — no drafting, no stress test. Useful for quickly understanding what reviewers want before deciding how to respond. - REBUTTAL_DIR =
rebuttal/ - RENDER_HTML = true — When
true(default), auto-renderrebuttal/REBUTTAL_DRAFT_rich.md(the detailed reviewer-facing draft) to HTML after Phase 6 / Phase 8 finalization. Uses full Codex review gate (final pre-submission deliverable — reviewer-facing content, render fidelity matters). The plain-textPASTE_READY.txtis NOT rendered (it's character-counted plain text by design). Setfalseto skip, or pass— render html: false.
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.
- 5d ago Changed 36b9f570181c
- 9d ago First seen · 377 lines · 73 tokens per session scan A e8d30a41aa66
rebuttal 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 73 tokens to every session and 5,863 once invoked, about $0.0004 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 2026-09-03.
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