“RAG”

Retrieves grounded answers from an approved Obsidian vault using bounded search, source citations, and explicit uncertainty.

Finalize guard for review, verification, anchoring, and metrics, including RAG/skills change branch.

Implement retrieval-augmented generation with document indexing and search.

Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns

Design Retrieval-Augmented Generation systems for private data.

给定 PMID / PMCID 取结构化全文（BioC XML/JSON）。封装 NLM BioC PubMed API（标题+摘要）和 BioC PMC OA API（开放获取全文），输出按章节切分的段落、表格上下文、RAG 友好 chunk。适合大批量入向量库。触发词：全文抽取 / BioC / RAG 入库…

AI 系统数据安全测试方法论。当需要评估 LLM/AI 系统的数据泄露风险、训练数据安全、 或 RAG/向量库数据完整性时触发。覆盖: System Prompt 泄露(元 Prompt/角色扮演/关键字定位)、 训练数据推导与提取、成员推断攻击、模型反演攻击、RAG 数据投毒、API 信息泄露、…

Web 与 AI 跨域攻击链方法论。当目标系统同时包含传统 Web 应用和 AI/LLM 组件、 需要评估 Web 漏洞对 AI 系统的影响或 AI 漏洞对 Web 系统的影响时触发。 覆盖双向攻击链: Web→AI(XSS 窃取对话/SSRF 调用模型 API/SQLi 污染 RAG/文件上传 RAG 投毒) 和…

Processar, comparar ou reindexar artigos acadêmicos no RAG MVLI com proveniência entre fonte, trecho e evidência.

LLM application architecture expert for RAG, prompting, agents, and production AI systemsUse when "rag system, prompt engineering, llm application, ai agent…

Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.

给定 PMID/PMCID 取结构化全文（NLM BioC XML/JSON）。封装 BioC PubMed（标题+摘要）和 BioC PMC OA（开放获取全文）端点，输出按章节切分的段落、表格上下文、RAG 友好 chunk，适合大批量入向量库。Fetches structured fulltext from…

Build AI features with Vercel AI SDK (chat, agents, RAG, tools).

AI/LLM应用攻击:提示注入,Agent工具滥用RCE,RAG投毒,MCP供应链,torch.load pickle RCE。Use when testing LLM apps, agents, RAG, MCP plugins, or AI model file risks.

Framework for building LLM-powered applications with agents, chains, and RAG.

High-performance vector similarity search engine for RAG and semantic search.

Data framework for building LLM applications with RAG.

any RAG prompt, before blaming the retriever or the model.

RAG Builder with Parallel Document Processing Vector database construction with local embeddings (zero cost) Handles PDF download, text extraction, chunking…

Use when a medical AI paper should be found and cited by AI search engines and RAG tools.

Use when splitting extracted text into chunks for LLM context windows or RAG ingestion.

Use when extracting keywords (YAKE/RAKE) from documents — and, secondarily, when detecting document language or generating embeddings for RAG and search.

Set up and optimize a complete local AI infrastructure — Ollama, Open WebUI, local RAG pipelines, and private model serving.

Chunking, embeddings, and RAG pipeline integration