research-cycle-skill 是做什么的？
Codex技能：闭环研究流程，包含多智能体对抗性空白发现、Semantic Scholar验证和7阶段研究循环
Codex技能：闭环研究流程，包含多智能体对抗性空白发现、Semantic Scholar验证和7阶段研究循环
kris-yun/research-cycle-skill 是一个专为Codex设计的AI智能体技能，它实现了一个闭环研究工作流。该技能的核心特色在于通过多智能体对抗性空白发现机制，自动识别研究领域中的知识缺口，并利用Semantic Scholar进行实时文献验证，确保输出的研究问题具有前沿性和准确性。整个流程分为7个严格的阶段：从问题定义、文献搜索、空白分析，到研究假设生成、实验设计、结果综合，以及最终的报告生成。每个阶段都设计了明确的输入输出和切换条件，使得AI智能体可以像人类研究员一样系统性地推进研究，同时降低幻觉风险。该技能特别适用于需要快速文献综述、研究提案撰写以及跨学科洞见发现的场景，能够显著提升研究效率和质量。
npx skills add kris-yun/research-cycle-skillInstalled? Explore more 研究与数据分析 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Codex技能：闭环研究流程，包含多智能体对抗性空白发现、Semantic Scholar验证和7阶段研究循环
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.