paper-deep-dive

以结构化、证据驱动、读者友好的方式深度解读单篇论文。用于用户要求论文深读、详细分析、博客级讲解、研究脉络梳理、方法架构拆解、关键概念解释，或判断实验是否真的支撑论文 claim；也用于基于论文 PDF、arXiv 页面、附录、官方代码和项目页完成系统性论文解读。

This skill is an instruction-only template for producing careful, evidence-tagged paper analyses and is internally coherent. Before installing or using it: (1) be mindful of any PDFs or private code you hand the agent — supplying confidential documents could expose them to networked model calls or logs; (2) the skill expects the agent to fetch/consume external resources when you provide links (arXiv, project pages, GitHub)—confirm your agent/network policies for outbound fetches; (3) the skill's framework reduces but does not eliminate LLM hallucination—always verify critical claim-to-evidence mappings against the original paper or code; (4) no credentials or system-level access are required, so there is low platform risk from the skill itself. If you need higher assurance, review sample outputs produced by the skill on public papers and confirm the agent will not automatically fetch resources you don't want shared.

Type: OpenClaw Skill Name: paper-deep-dive Version: 1.0.0 The paper-deep-dive skill bundle is a comprehensive and well-documented framework designed to guide an AI agent in performing structured, evidence-based academic paper analysis. The instructions in SKILL.md and the supporting reference files (e.g., evidence-rules.md, output-template.md) focus entirely on improving the quality, transparency, and critical analysis of research summaries. There are no indicators of data exfiltration, malicious command execution, or harmful prompt injection; the bundle promotes honest reporting of uncertainties and rigorous mapping of claims to evidence.