Skill UI

This tool executes the advanced `metaharness/darwin` security benchmark (ADR-155). It rigorously evaluates the security detection capabilities of AI systems against a controlled ground-truth corpus, providing critical metrics like TPR, FPR, and patch-pass rates. It compares the champion model against multiple baselines, offering empirical data crucial for iterative security improvement and drift detection.

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.

This tool aggregates various system metrics—including counters, gauges, and histograms—from the observability namespace. It calculates baselines, flags significant deviations (anomalies) based on statistical standards, and provides a comprehensive system health report. Use it to get a detailed snapshot of system performance, monitor for degradation, and ensure overall system stability.