Skill UI

This comprehensive skill guides the systematic optimization of end-to-end application performance across the entire stack. It covers establishing performance baselines through deep profiling (CPU, Memory, I/O), assessing observability gaps, optimizing databases, tuning backend services and APIs, and enhancing frontend rendering. Ideal for complex, scalable applications that require measurable performance improvements, reliability enhancements, and adherence to modern user experience standards.

This comprehensive skill guides the systematic optimization of end-to-end application performance across the entire stack. It covers establishing performance baselines through deep profiling (CPU, Memory, I/O), assessing observability gaps, optimizing databases, tuning backend services and APIs, and enhancing frontend rendering. Ideal for complex, scalable applications that require measurable performance improvements, reliability enhancements, and adherence to modern user experience standards.

Automatically analyzes CI/CD performance metrics, compares them against baselines and thresholds, and flags statistical regressions so teams catch response-time or throughput degradations before deployment.

A comprehensive checklist guiding the full lifecycle deployment of Exa search integrations to a production environment. It covers critical stages including pre-deployment security audits, code quality verification, performance baselines, monitoring setup, and emergency rollback procedures. Use this guide when preparing for a major launch or verifying production readiness.

Execute comprehensive load, stress, and soak tests to validate API performance, identify scaling bottlenecks, and establish throughput baselines. This tool simulates realistic, high-volume traffic patterns using industry-standard tools like k6 or Artillery, allowing users to define complex scenarios (e.g., e-commerce checkout flows, traffic spikes) and assert performance thresholds (p95 latency, error rates). It helps pinpoint resource limitations in databases, network layers, or compute resources, ensuring the API is ready for production scalability.

This deep dive guide provides comprehensive strategies and detailed checklists for migrating to the OpenEvidence platform. It covers various migration methods, such as parallel run, Strangler fig, and Big bang approaches. The guide assists technical teams in planning the transition by addressing key areas including API mapping, data migration plans, rollback procedures, and performance baselines, ensuring a secure and smooth transition for healthcare IT systems.

Runs automated visual regression tests via Percy, Chromatic, or BackstopJS to capture component or page screenshots, compare them against baselines, and flag unintended UI changes during feature updates or CI/CD runs.

This skill guides the detection of unintended visual changes in user interface (UI) components. It works by capturing screenshots and performing pixel-by-pixel comparisons against approved baselines. It is essential for ensuring UI consistency across code changes, responsive breakpoints, and differing environments. Supports modern tools like Playwright, Percy, and BackstopJS.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

This command executes the entire AgentHub lifecycle in a single, chained invocation. It automates complex development tasks by sequentially handling initialization, capturing performance baselines, spawning multiple specialized agents, evaluating the results (via metrics or LLM judging), and finally presenting a winner for merging. Use this when running an end-to-end competition or complex refactoring task.

This tool detects abnormal access patterns across major cloud storage services (AWS S3, GCS, Azure Blob Storage). By analyzing audit logs like CloudTrail, it uses statistical baselines and time-series anomaly detection to identify suspicious activities such as after-hours bulk downloads, access from new IPs, or unusual API call spikes. It is crucial for investigating potential data exfiltration incidents or building robust cloud security detection rules.

This tool parses NetFlow v9 and IPFIX records to conduct deep network security analysis. It is crucial for investigating security incidents by building traffic baselines and applying statistical methods. Key detection capabilities include identifying port scanning, data exfiltration attempts, command and control (C2) beaconing patterns, and general volumetric anomalies in network traffic.