{"id":"wanshuiyin-auto-claude-code-research-in-sleep","name":"Auto-claude-code-research-in-sleep","af_score":48.5,"security_score":45.8,"reliability_score":25.0,"what_it_does":"Auto-claude-code-research-in-sleep (ARIS) is a collection of Claude Code–style “skills” (Markdown-based workflows) that orchestrate an autonomous ML research pipeline: idea discovery, cross-model review loops (Claude Code as executor, an external LLM via MCP/Codex-style reviewer), experiment planning/bridging, paper writing, and post-submission rebuttal plus slide/poster generation. It also documents adaptations for other agent IDEs (Cursor, Trae, Antigravity, OpenClaw) and supports alternative model combinations via OpenAI-compatible APIs (as a reviewer).","best_when":"You want an agent workflow you can plug into Claude Code (or adapt to other IDEs) to run structured research-to-paper iterations with a separate reviewer model, while keeping artifacts as plain files (Markdown/outputs).","avoid_when":"You need a stable, versioned hosted API with documented SLAs, or you cannot provide external LLM credentials/tooling for the reviewer/execution environments.","last_evaluated":"2026-03-29T15:00:55.234722+00:00","has_mcp":true,"has_api":false,"auth_methods":["codex setup / codex mcp (via Claude Code MCP integration)","OpenAI-compatible API credentials for reviewer models (alternative model combinations)","IDE-specific configuration for integrations/adaptations (Cursor/Trae/Antigravity/OpenClaw)"],"has_free_tier":true,"known_gotchas":["Because workflows drive code cloning, experiment execution, and multi-step document generation, failures may require manual intervention (the provided excerpt doesn’t describe robust retry/idempotency semantics).","Cross-model pipelines can fail if the reviewer/executor toolchains (MCP/Codex/OpenAI-compatible API) are not correctly configured.","Auto-experiment and rebuttal pipelines depend on accurate mapping of claims/concerns; the README mentions safety gates, but operational failure modes and recovery steps aren’t detailed in the provided content."],"error_quality":0.0}