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RAG 구현
RAG 구현
이 스킬은 Dennis0424/claude-skills를 기반으로 한 RAG(Retrieval-Augmented Generation) 구현입니다. RAG는 검색 기반 생성 모델로, 외부 지식 소스에서 관련 문서를 검색하여 AI 모델의 응답을 보강합니다. 주로 Claude Code, Cursor, Codex 등 AI 에이전트 플랫폼에서 사용되며, 개발자 도구와의 통합을 통해 정확하고 맥락에 맞는 답변을 제공합니다. SKILL.md 문서에는 RAG 파이프라인의 구성 요소와 사용 방법이 설명되어 있습니다.
npx skills add Dennis0424/claude-skillsInstalled? Explore more 연구 & 데이터 분석 skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
RAG 구현
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.