Skill UI

A comprehensive tool for designing, generating schemas, and evaluating complex multi-agent systems. It guides users through defining system requirements, selecting optimal orchestration patterns (Supervisor, Swarm, Pipeline), generating provider-ready tool schemas (Anthropic/OpenAI), and analyzing execution logs for performance bottlenecks, cost, and latency. This ensures robust, production-grade autonomous workflows.

This is the .NET SDK for managing Arize AI Observability and Evaluation resources within Azure Resource Manager (ARM). It provides programmatic access to create, read, update, and delete AI organization resources. It is essential for MLOps professionals and developers who need to monitor, track, and evaluate the performance of AI models deployed on Azure.

A comprehensive toolkit for evaluating the performance of code generation models. It supports benchmarking against industry standards like HumanEval, MBPP, and MultiPL-E across multiple languages. Use this tool to quantitatively compare different LLM coding abilities and measure code synthesis quality using pass@k metrics.

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

Provides comprehensive guidance on building robust evaluation frameworks for complex agent systems. It addresses the inherent challenges of non-determinism and multiple valid paths, enabling systematic performance testing, validating context engineering choices, and measuring continuous improvements across multiple quality dimensions (e.g., factual accuracy, tool efficiency).

Master comprehensive strategies for evaluating Large Language Model (LLM) applications. This skill covers automated metrics (BLEU, ROUGE, BERTScore), structured human evaluation dimensions, and advanced LLM-as-Judge techniques. Use it to systematically measure model performance, compare prompts, detect regressions, and build confidence in production systems.

Monitor the operational health of PostHog integrations by tracking critical metrics. This skill checks event ingestion rates, feature flag evaluation latency, and monitors billing volume consumption. It also provides rules for Prometheus/Grafana alerting, ensuring data pipelines remain stable and alerting on potential performance bottlenecks or exceeding API limits.

This comprehensive guide outlines advanced strategies for optimizing PostHog in production environments. Key techniques covered include implementing local feature flag evaluation to eliminate network latency, optimizing event batching for network efficiency, utilizing strategic event sampling, and writing efficient HogQL queries. Master these methods to dramatically improve data collection reliability and query speed.

This skill specializes in designing, optimizing, and evaluating prompts for Large Language Models (LLMs). It allows users to generate optimized prompt templates, structured output schemas (e.g., JSON mode, function calling), and comprehensive test suites. Use this skill when building new LLM applications, implementing complex reasoning patterns like Chain-of-Thought, defining system guardrails, or building robust evaluation frameworks to measure and improve model performance.

This skill simulates the role of a quantitative analyst, providing comprehensive tools for building complex financial models, backtesting trading strategies, and analyzing market data. It covers advanced topics like portfolio optimization (Markowitz, Black-Litterman), calculating risk metrics (VaR, Sharpe ratio), and performing statistical arbitrage. Ideal for quantitative finance professionals needing rigorous data analysis, robust backtesting, and risk-adjusted performance evaluation.

Guidelines for reviewing UI code against web interface best practices, covering accessibility, performance, responsive design, component patterns, CSS practices, security, and internationalization during audits or UX evaluations.

This skill provides comprehensive tools for documenting and managing model performance on Hugging Face. It supports multiple methods for adding structured evaluation data, including extracting tables from README content, importing benchmark scores via APIs (like Artificial Analysis), and executing high-performance custom evaluations using advanced backends such as vLLM or lighteval. It ensures that all metadata is structured and compatible with model leaderboards and industry standards.