Skill UI

Extracts cognitive fingerprints from any text using the DHDNA framework to reveal reasoning styles, decision habits, values, and emotional tones, ideal for profiling authors, comparing thinkers, or unpacking how someone reasons.

This skill provides continuous, structured monitoring of deep learning model training metrics. It analyzes data from platforms like WandB and local logs to detect critical anomalies such as NaN values, divergence, plateaus, and sudden spikes. By providing a clear decision (CONTINUE, WAIT, or STOP) and detailed evidence, it ensures compute resources are only spent on viable training runs.

This guide addresses critical LangGraph streaming pitfalls, including state overdraw, proxy buffering issues (Nginx, Cloud Run), and excessive event noise. It provides a production-grade pattern for implementing robust SSE/WebSocket endpoints using FastAPI. Learn how to correctly select the `stream_mode` (messages, updates, values) and implement server-side event filtering to reliably build live-token chat UIs and perform comprehensive state visualization.

This skill processes clinical text to interpret laboratory results. It parses reference ranges (e.g., "135-145", "<5") and derives structured abnormal flags (low, normal, high, critical) for a given numeric value. It is unit-agnostic and respects explicit flags provided by the originating lab, making it crucial for structured clinical data extraction and interpretation workflows.