“RAG”

Retrieve relevant excerpts from a project's text files without loading entire files into context.

Build retrieval and RAG with Anvia — chunking, embeddings, vector stores, knowledge graphs, filters, and search tools.

Use for RAG pipelines, retrieval augmented generation, ColBERTv2, context retrieval, multi-hop RAG, and grounded DSPy answers.

Design RAG pipelines: chunking, retrieval evaluation, and architecture.

Use when comparing basic, enhanced, GraphRAG, or agentic RAG designs, evaluating domain-specific RAG quality, tuning retrieval components, or deciding whether…

Use this skill when you need evidence-bounded grounding, relevance, completeness, citation support, abstention, and answer-level evidence in RAG outputs…

Private knowledge base for indexing multimodal files or folders into a knowledge graph, supporting multi-hop graph retrieval

Use this skill when you need evidence-bounded query variants, chunking, filters, recall/precision proxies, ranking, freshness, and retrieval evidence…

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

Test RAG vector stores (Pinecone, Qdrant, Weaviate, Chroma, pgvector, FAISS) for embedding inversion, cross-tenant data leakage, and data poisoning per OWASP…

将 PDF/PPT/Excel/Word 等多格式文档解析为结构化 Markdown，并输出元数据与解析置信度，作为 RAG 与四色卡片的数据底座。

Build, review, or harden AI agents, LLM apps, RAG, tool-calling, coding agents, and agentic workflows with secure-by-default controls.

Evaluate Anvia agents and retrieval — deterministic metrics, semantic similarity, LLM judges, RAG quality, and CLI eval runs.

Build RAG / unstructured-document evaluation datasets and demo documents (e.g.

Use when implementing GPT chat, streaming, function calling, embeddings for RAG, images, audio or batch jobs, or troubleshooting 429 rate limits and API or…

Use AdalFlow to build, debug, evaluate, optimize, trace, and operate LLM task pipelines, RAG systems, agents, tools, and structured-output workflows.

AgentScope repo skill for building agents, provider connectors, RAG and memory workflows, service deployments, and local or sandboxed workspaces.

Routes agents using or contributing to CrewAI, including crews, flows, CLI projects, tools, MCP, memory, RAG, LLM providers, observability, files, multimodal…

Route DeepSearcher workflows for provider setup, data ingestion, RAG querying, CLI and service operation, and evaluation.

Deep-crawl any website from start URLs, return per-page LLM-ready text/markdown/HTML plus metadata (title, description, author, language, canonical URL, OG)…

Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search (semantic), and Hybrid Search.

Suede AI code review and A-F ship grade for a diff, PR, branch, or commit range: findings ranked P0-P3 with file:line evidence and a fix path, an A-F grade…

Use FlashRAG for retrieval-augmented generation research workflows: config/data validation, retrieval and index building, generation/refinement components…

Local document and PDF parsing that returns spatial text with bounding boxes.