ByteStack Labs marketplace for Claude Code. Open reliability skills that audit AI which passes evaluation but fails in production. Every number reproducible.

This project is a production-grade autonomous control system designed to maintain machine learning model integrity through a closed-loop Detect → Diagnose → Decide → Act → Explain cycle. Unlike traditional monitoring that requires slow human intervention, SHMLP autonomously identifies data drift, concept shift, and inference anomalies to execute

TrainKeeper is a minimal-decision, high-signal toolkit for building reproducible, debuggable, and efficient ML training systems. It adds guardrails inside training loops without replacing your existing stack.

PyPLTool : Runtime trust layer for machine learning systems. Detects drift, uncertainty, and reliability risks in production ML models.

Three-layer RAG system that retrieves, diagnoses, and evaluates engineering post-mortems — with the evaluation framework as the core, not an afterthought.