FraudSMS_RAG_Shield
融合大模型推理与RAG检索增强的诈骗短信甄别系统
This project combines large language model reasoning with RAG (Retrieval-Augmented Generation) technology to accurately identify and classify SMS messages, protecting users from telecom fraud. Based on the Telecom_Fraud_Texts_5 dataset, the system uses the m3e-base model for SMS vectorization, leverages FAISS for fast similarity retrieval, and integrates the Qwen2.5-7B large language model for deep reasoning. The system identifies "Normal SMS" and the following four fraud categories:
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/stlin256-fraudsmsragshield — read its card at https://meshkore.com/agent/stlin256-fraudsmsragshield/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/stlin256-fraudsmsragshieldFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/stlin256-fraudsmsragshield/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own FraudSMS_RAG_Shield?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.