Skill UI

This skill automates the entire SOC 2 Type II audit lifecycle. It facilitates gap assessment against AICPA Trust Services Criteria (CC1-CC9) and collects continuous evidence from major cloud providers (AWS, Azure, GCP) and identity systems (Okta, Jira). It supports validating control effectiveness, remediation tracking, and overall compliance monitoring for Security, Availability, Processing Integrity, Confidentiality, and Privacy.

This skill provides interactive, periodic monitoring of deep learning training runs. It continuously checks critical metrics—such as loss trends, evaluation scores, and gradient norms—against defined baselines to detect signs of divergence, NaN values, plateaus, or general instability. It delivers a structured report and recommends whether to continue, wait, or stop the training process, ensuring computational resources are not wasted on failing models.

This skill monitors for regressions on deployed URLs following deployments, merges, or dependency upgrades. It performs comprehensive checks including HTTP status, console errors, network failures, performance metrics (LCP/CLS/INP), and crucial API endpoint health, ensuring service stability in production or staging environments. Supports quick checks, continuous monitoring, and differential comparison against baselines.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

A comprehensive guide for integrating Firebase Crashlytics into Flutter applications. It teaches how to capture fatal, non-fatal, and isolate errors, customize reports with user identifiers, custom keys, and logs, and manage opt-in data collection to ensure robust crash monitoring in production environments.

This skill provides comprehensive management for annotation schemas (configs) and human review workflows (queues) within the Arize platform. It supports defining categorical, continuous, and freeform label types. Users can programmatically apply these annotations to project spans and datasets using the Python SDK, facilitating structured human feedback loops and improving model monitoring.

This skill generates deep, clickable URLs to various resources within the Arize monitoring UI. It supports linking to specific traces, spans, sessions, datasets, evaluators, and annotation configurations. It is essential for sharing specific debugging artifacts or navigating directly to key data points within the MLOps platform by requiring precise resource IDs and time ranges.

A powerful tool utilizing Rube MCP to automate interactions with Make (formerly Integromat) scenarios. It allows users to retrieve detailed operation logs, monitor usage data, and validate critical configuration parameters such as supported languages and timezones. Ideal for building comprehensive workflow monitoring dashboards and ensuring the integrity of complex integration pipelines.

This skill guides users on setting up and running LLM-as-judge evaluations using Arize. It covers defining evaluators (including prompts, classification choices, and models) and executing tasks against real data spans or experiments. It supports key metrics like hallucination, faithfulness, and correctness, and facilitates continuous monitoring for comprehensive LLM performance analysis.

Reviews Python + BigQuery pipelines to flag runaway costs, missing dry-run safeguards, unsafe backfills, write idempotency issues, and monitoring gaps, returning a structured report with exact function/line references for minimal fixes.

Provides comprehensive, enterprise-level monitoring for all Power Automate flows across an entire tenant. Using a cached store, it tracks failure rates, run health trends, asset inventory (makers, apps, connections), and compliance reports without hitting API rate limits. Ideal for governance teams needing an aggregated view of platform health and root-cause analysis.

This guide provides strategies to optimize the usage and costs associated with the Canva Connect API. It covers best practices such as aggressive metadata caching, implementing export tracking to prevent duplicates, managing pagination efficiently, and monitoring API call volume to stay within rate limits and budget constraints.