自睡眠自研究系统 是 AI Skill Hub 本期精选MCP工具之一。已获得 9.0k 颗 GitHub Star,综合评分 8.5 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
自睡眠自研究系统 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
自睡眠自研究系统 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep
# 方式二:手动配置 claude_desktop_config.json
{
"mcpServers": {
"--------": {
"command": "npx",
"args": ["-y", "auto-claude-code-research-in-sleep"]
}
}
}
# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
# 安装后在 Claude 对话中直接使用 # 示例: 用户: 请帮我用 自睡眠自研究系统 执行以下任务... Claude: [自动调用 自睡眠自研究系统 MCP 工具处理请求] # 查看可用工具列表 # 在 Claude 中输入:"列出所有可用的 MCP 工具"
// claude_desktop_config.json 配置示例
{
"mcpServers": {
"________": {
"command": "npx",
"args": ["-y", "auto-claude-code-research-in-sleep"],
"env": {
// "API_KEY": "your-api-key-here"
}
}
}
}
// 保存后重启 Claude Desktop 生效
<p align="center"> <a href="https://huggingface.co/papers/2605.03042"> <img src="docs/hf_daily_paper_1.svg" alt="Hugging Face Daily Paper · #1 Paper of the Day" width="360"> </a> </p>
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· 💬 Join Community ·
💡 Use ARIS as a skill-based workflow in Claude Code / Codex CLI / Cursor / Trae / Antigravity / GitHub Copilot CLI / OpenClaw / DeepSeek Harness, or get the full experience with the standalone ARIS-Code CLI — enjoy any way you like!
🐋 On DeepSeek Harness it installs as one plugin: dsh plugin --profile web add dsh-aris (fetches from npm by itself — no separate install step, but pnpm must be on PATH) — all 82 skills unchanged, Codex still the independent reviewer. Setup and limits on the dsh-aris branch.
🌱 ARIS is a methodology, not a platform. What matters is the research workflow — take it wherever you go.
🤖 AI agents: Read AGENT_GUIDE.md instead — structured for LLM consumption, not human browsing.
🛡️ ARIS audits its own output → now Anti-Autoresearch audits everyone's. 61 signals — 46 integrity hack-patterns in 8 families, 13 AI-style impressions, 2 advisory — checked end-to-end into a deterministic, reviewer-ready report. Self-consistency + fabrication forensics, not an AI-text detector.
<p align="center"><em>The field has put up with unreliable autoresearch long enough —<br>Anti-Autoresearch is the read that finally catches it.</em></p>
🧱 ARIS's reviewer is good — and it also proposed hashes nobody reads → HERO is the contract that stops that. Hashing, Edge cases, Rubrics, Overbuild — the four shapes agents over-defend in, as a ~550-token block for CLAUDE.md / AGENTS.md. It bounds what the agent proposes, never what it looks for.
🎬 ARIS goes multimodal → ARIS-Movie-Director — hand it a rough story and get back a movie told in still frames, checked scene by scene (the reference run has 19 scenes). Long stories usually break when the model forgets earlier details or judges its own work — so ARIS keeps a research-wiki for memory and has other models check every frame.
<details> <summary>🗺️ <b>Method figure</b> — story brief → authored source of truth → per-panel audited spiral → assembly & release, on one canvas</summary>
<p align="center"> <a href="https://github.com/wanshuiyin/ARIS-Movie-Director"> <img src="docs/aris-movie-director-method.png" alt="ARIS-Movie-Director method — the audited spiral: authored source of truth (asset library · outline · storyboard · comic.json) → per-panel image_gen + cross-model panel_gate (blind token-diff, single-vote veto) → research-wiki audit trace → assembly + release" width="100%"> </a> </p>
</details>
🧭 The same loop also makes clean method / flow diagrams — the figure above was made with it. Entry points in ARIS-Movie-Director:/movie-pipelineand/method-figure, the skill that made this figure.
<details> <summary>🎞️ <i>A few frames from the reference movie — the story's own integrity beat: a run that <b>reported <code>+6.2</code></b> but <b>really moved <code>+1.4</code></b>.</i> <b><a href="https://wanshuiyin.github.io/ARIS-Movie-Director/comic/">▶ watch all 19 scenes →</a></b></summary>
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🎯 准备 2026 AI 秋招? → 🌐 ARIS-in-AI-Offer · GitHub repo · 中文 README —— 23 篇双语 ML / LLM / 多模态 / 生成式 / Agent 面试 cheat sheet,每篇 = 公式推导 + 从零 PyTorch + 25 高频面试题(L1 / L2 / L3),全部由 ARIS 的 /render-html 自动生成。希望大家秋招轻松一点 🌱
<details> <summary><b>🖼️ Preview</b> — the three-pillar cheat-sheet strip (① Foundations · ② Interview Q&A · ③ From-Scratch Code)</summary>
<p align="center"> <a href="https://github.com/wanshuiyin/ARIS-in-AI-Offer"> <img src="https://raw.githubusercontent.com/wanshuiyin/ARIS-in-AI-Offer/main/assets/preview_strip.jpg" alt="ARIS-in-AI-Offer preview — ① Foundations + ② Interview Q&A + ③ From-Scratch Code, three columns from a representative cheat sheet" width="100%"> </a> </p>
</details>
📝 Three long-form blogs, cross-model collaborative writing via /render-html — Continuous DLM — a representation-perspective survey (2026 H1) · Cosmos 3 — understanding + generation in one Transformer (MoT) · Diffusion × representation × manifold learning.
🛰 Keep an eye on your agent windows — Claude Fleet (by @tianyilt; local read-only dashboard for many parallel Claude Code / Codex windows, full-text transcript search — worth a ⭐), or the lighter built-in ARIS-Monitor (a tiny always-on-top macOS widget that lights up 🔴 when a session waits for your approval; click to jump there).
<details> <summary><b>🖼️ Preview</b> — Claude Fleet dashboard (full web) & ARIS-Monitor widget (minimal, built-in)</summary>
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| Claude Fleet · 全功能网页看板 | ARIS-Monitor · 极简悬浮小窗(自带) |
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<details> <summary><b>Run either in seconds</b> — ARIS-Monitor (5s) / Claude Fleet (30s)</summary>
ARIS-Monitor — built-in, no clone / no pip / no browser:
```bash cd aris-monitor && ./run.sh
⚠️ Any entry that touches skills: bash tools/smart_update.sh --apply pulls it.
gpt-6-astra. Every reviewer call that pinned gpt-5.6-sol now pins gpt-6-astra; the two effort tiers (ultra for the seven deep audits, xhigh everywhere else) are unchanged, and so is the executor — whatever agent you run ARIS in. No access to the model yet? The capability fallback tries gpt-5.6-sol, then gpt-5.5, both at xhigh — nothing to configure..github/agents/ whether or not anything used them — dead files for Claude Code and Codex users, broken links if you committed them. Now they are deployed only while auto-review-loop is installed; --no-agent-profiles switches them off for good (undo with --agent-profiles). Your next re-run removes the links the installer created earlier; files you wrote yourself are never touched./auto-paper-improvement-loop flags the same defects. Indexed under Awesome Community Skills./research-review — the one reviewer prompt that never got the scope-limits block — now carries it, and its brief ends with "if the work holds up, say so clearly" instead of bare brutality; the adversarial stance itself is untouched. /research-pipeline no longer dies overnight on a missing VENUE: ideas, experiments, analysis and the narrative report are venue-independent and run to completion — only paper formatting defers, stamped clearly for a later resume. It had also been declaring VENUE = ICLR while forbidding silent defaults; the default is gone.活跃开源项目,9k星体现社区认可。MCP框架设计先进,Markdown技能易扩展,是AI自动化研究的创新工具。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,自睡眠自研究系统 在MCP工具赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | Auto-claude-code-research-in-sleep |
| 原始描述 | 开源MCP工具:ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomo。⭐9.0k · Python |
| Topics | 自主智能体代码研究MCP工具自动化Claude扩展 |
| GitHub | https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep |
| License | MIT |
| 语言 | Python |
收录时间:2026-05-13 · 更新时间:2026-05-16 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
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