What does knowledge-base-analysis-advice do?
A Codex Skill for analyzing knowledge bases and RAG real-world Q&A tests, tracing responsibility layers, and generating actionable optimization recommendations.
A Codex Skill for analyzing knowledge bases and RAG real-world Q&A tests, tracing responsibility layers, and generating actionable optimization recommendations.
This skill enables AI agents to analyze knowledge bases and real-world Retrieval-Augmented Generation (RAG) Q&A test results. It helps trace issues back to specific responsibility layers—such as data ingestion, retrieval, or generation—and provides concrete, executable optimization advice. Designed for Codex, it supports debugging and improving RAG systems by identifying root causes of inaccuracies or inefficiencies. The skill processes test data, maps failures to system components, and suggests improvements like chunking strategies, embedding models, or prompt refinements.
npx skills add Knjss-0508/knowledge-base-analysis-adviceInstalled? Explore more Research & Data Analysis skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
A Codex Skill for analyzing knowledge bases and RAG real-world Q&A tests, tracing responsibility layers, and generating actionable optimization recommendations.
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.