AI Deep Learning Methodology
v1.0.0用AI系统化深度学习任意领域的方法论。通过"引文网络提纯+多AI交叉质询"构建结构化知识体系。 当用户说"想学XX领域"、"系统了解XX"、"帮我研究XX"、"构建XX知识体系"、"深度学习XX"时使用。 适用于：新领域入门、学术文献调研、构建个人知识库、准备专业讨论。
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byshenghui@facadefish
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (systematic domain learning via citation networks + multi-AI interrogation) match the SKILL.md. All recommended actions (finding key papers, building a NotebookLM/RAG knowledge base, running multi-AI question loops) are directly relevant. The skill requests no unrelated binaries, env vars, or config paths.
Instruction Scope
Instructions are focused on search, download, ingestion into NotebookLM (or similar), and multi‑AI interrogation; this is expected. Two points for the user to note: (1) it assumes the agent or helper agents will download full-text papers from external sources (Google Scholar, arXiv, CNKI, project sites) which may involve paywalls or copyright-sensitive content; (2) it instructs uploading documents to NotebookLM/RAG tools — that has privacy and data-sharing implications. No instructions ask the agent to read unrelated local files or system secrets.
Install Mechanism
Instruction-only skill with no install spec and no code files — lowest-risk installation footprint.
Credentials
The skill declares no environment variables, credentials, or config paths. Recommended external services (NotebookLM, Google Scholar, arXiv, CNKI) are logical for the task and do not require the skill itself to request secrets.
Persistence & Privilege
always: false and no instructions to modify agent/system-wide configs. The skill does not request persistent privileges or background presence.
Assessment
This skill is coherent and appears to do what it says, but consider these practical risks before use: (1) Privacy: uploading papers or notes to NotebookLM/RAG tools shares their contents with that service — avoid uploading confidential or proprietary documents unless permitted. (2) Copyright/paywalls: downloading full-text papers (especially from CNKI or publisher sites) may be paywalled or restricted — ensure you have lawful access. (3) Multi‑AI workflow costs/credentials: using multiple models (GPT/Claude/Gemini) requires separate accounts/API keys outside the skill; the skill does not provide or request them. (4) Verify citations: always spot-check extracted citation counts, quotes, and chains — cross-check original PDFs. If you want higher assurance, ask the author/maintainer for provenance (source/homepage) or request the skill be extended to explicitly document how it handles paywalled sources and user data uploads.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.