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/proof-checkerWrote 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/proof-checker)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/proof-checker"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/proof-checker/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/proof-checker"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/proof-checker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
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 →
- high Anti-Refusal · line 166 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
- 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.
- medium Excessive Agency · line 71 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.
- medium Memory Poisoning · line 659 Skill injects content designed to persist in agent memory or context across interactions. Persistent injection can alter agent behavior long after the initial interaction.Fix: Do not allow untrusted input to persist in agent memory or context. Validate all content before storing and implement memory isolation between sessions.
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 | $0.00087 | $0.14164 |
| Opus 5.5 | $0.00035 | $0.05666 |
| Sonnet 5.5 | $0.00017 | $0.02833 |
| Haiku 4.5 | $0.00009 | $0.01416 |
Grade A, and why
proof-checker 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 2d 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 — 901 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proof Checker: Rigorous Mathematical Verification & Fixing
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 is verdict-bearing — it judges proof validity across rounds, threading the reviewer's memory from Phase 1 → Phase 3 viacodex-replyso the reviewer can check whether a fix actually closed the gap it flagged. An external timer re-enters from the top each tick, starting a fresh thread and losing that memory. Schedule the external wait that precedes it, not the verdict. Seeshared-references/external-cadence.md.
Systematically verify a mathematical proof via cross-model adversarial review, fix identified gaps, re-review until convergence, and generate a detailed audit report with proof-obligation accounting.
Context: $ARGUMENTS
Constants
- MAX_REVIEW_ROUNDS = 3
- REVIEWER_MODEL =
gpt-6-astra— Default model for the Codex backend, reasoning effortultra(deep-audit tier; pershared-references/reviewer-routing.md, capability fallbackgpt-6-astra+xhighthen the model capability fallbacks starting withgpt-6.1-sol+xhigh, capability errors only — never belowxhigh). Manual backend uses a model the user chooses, but it must be a non-Claude model ARIS can classify (OpenAI, Google, DeepSeek, Moonshot/Kimi, Qwen) — the executor is Claude, so routing the proof review into any Claude product makes Claude judge Claude and voids the cross-model invariant (seeshared-references/reviewer-routing.md). - REVIEWER_BACKEND =
codex— Default: Codex MCP (ultra). 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.
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.
- 2d ago Changed 36b0d62ed059
- 7d ago Changed · +22 lines 2a4a1846cd19
- 20d ago Changed · +12 lines 75ce2f5a4667
- 1mo ago Changed 65900678dc8d
- 1mo ago First seen · 867 lines · 87 tokens per session scan A 533b0eed5717
proof-checker is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (18,117 stars, last pushed 2d ago), licensed MIT. It adds 87 tokens to every session and 14,164 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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