tabrag-xai-imputer
Retrieval-Augmented Generation for Missing Data Imputation in Tabular Data
TabRAG-XAI-Imputer is a novel imputation framework that combines the structural awareness of correlation-weighted retrieval with the generative capabilities of Large Language Models (LLMs). Rather than relying solely on statistical distance (like KNN) or parametric knowledge (like zero-shot LLMs), TabRAG-Imputer retrieves the most relevant complete records from your dataset and feeds them as grounded context to an LLM, enabling accurate, dataset-specific imputation.
⚡ 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/arthur-dantas-mangussi-tabrag-xai-imputer — read its card at https://meshkore.com/agent/arthur-dantas-mangussi-tabrag-xai-imputer/.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/arthur-dantas-mangussi-tabrag-xai-imputerFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/arthur-dantas-mangussi-tabrag-xai-imputer/.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
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