Skill UI

This is the Azure Resource Manager SDK for .NET, designed to streamline the deployment and management of Weights & Biases (W&B) ML experiment tracking instances directly through Azure Marketplace. It allows developers to programmatically create, configure, and manage W&B environments, including setting up marketplace details, managing admin users, configuring Single Sign-On (SSO), and ensuring robust ML observability within an Azure ecosystem. Ideal for MLOps pipelines.

This is the Azure Resource Manager SDK for .NET, designed to streamline the deployment and management of Weights & Biases (W&B) ML experiment tracking instances directly through Azure Marketplace. It allows developers to programmatically create, configure, and manage W&B environments, including setting up marketplace details, managing admin users, configuring Single Sign-On (SSO), and ensuring robust ML observability within an Azure ecosystem. Ideal for MLOps pipelines.

This skill guides the construction of robust, production-grade backtesting systems for financial trading strategies. It helps users systematically validate performance, avoid common biases (like look-ahead bias), and implement advanced techniques such as walk-forward analysis, ensuring reliable performance estimates before live deployment.

HR-Pro is a comprehensive, ethical, and compliance-aware subagent designed to guide users through the entire employee lifecycle. It provides best practices, standardized checklists, and templates for key HR processes, including hiring and recruiting, onboarding/offboarding, performance management, PTO/leave policy, and complex employee relations. It prioritizes legal compliance and minimizing bias, while always advising consultation with local legal counsel for high-risk actions.

This skill provides comprehensive support for designing, calibrating, and standardizing entire role-based interview loops and hiring pipelines. It helps users structure competency assessments, generate targeted questions, reduce interviewer bias, and ensure the hiring process is consistent, objective, and aligned with core role competencies. Ideal for HR teams, hiring managers, and talent development professionals.

SAELens provides a framework for training and analyzing Sparse Autoencoders (SAEs). SAEs decompose the dense, often polysemantic activations of large language models into sparse, monosemantic features. Use this when you need to discover the discrete, interpretable concepts a model has learned, study feature superposition, or analyze specific safety-relevant behaviors (like bias or deception) within deep neural networks.

Assesses AI/ML models and datasets for ethical risks, fairness concerns, and bias, generating reports and mitigation guidance via the ai-ethics-validator plugin whenever a user requests an ethics review or bias detection.

Weights & Biases (W&B) automates experiment logging, real-time dashboards, hyperparameter sweeps, artifact tracking, and model registry/versioning for ML teams using PyTorch, TensorFlow, or HuggingFace workflows.

Assess research claims and experimental rigor by evaluating methodology, statistical validity, evidence quality, and biases using GRADE and Cochrane ROB frameworks, plus supporting schematics for complex concepts.

Guides using SHAP to explain ML predictions, compute feature importance, generate diagnostic plots, debug models, analyze bias, and compare models across tree, deep, linear, or black-box architectures.

Helps product teams structure customer discovery interviews by clarifying goals, segments, constraints, and adaptive methods so each session yields actionable insights instead of confirmation bias or surface-level feedback.

A comprehensive guide to building production-grade evaluation systems for Large Language Models (LLMs). This skill covers advanced methodologies, including Direct Scoring and Pairwise Comparison, alongside critical techniques for mitigating systematic biases (e.g., Position Bias, Length Bias). Learn how to select appropriate metrics and structure prompts to ensure reliable, consistent, and objective quality assessment across various AI applications.