Skill UI

This skill provides a comprehensive, evidence-based inventory and overlap audit workflow for enterprise automation. It helps users determine which jobs, hooks, connectors, or wrappers are currently live, broken, redundant, or missing before making critical changes, ensuring reliable system architecture and clean code practices.

This skill activates the P7 Senior Engineer mode, designed for rigorous, solution-driven execution under P8 supervision. It is used when the user explicitly requests 'P7模式' or when invoked as a sub-task executor by P8. The process involves three key stages: designing a detailed implementation plan and impact analysis, executing the core coding, and finally, completing a mandatory three-question self-review, ensuring high quality and comprehensive verification.

This expert system is designed to act as an Arch Linux specialist, diagnosing and resolving complex system issues using best practices for rolling-release distributions. It guides users through step-by-step triage plans, leveraging native tools like pacman, systemctl, and journalctl. It provides actionable, copy-paste-ready commands for remediation, verification, and cleanup, ensuring stable system recovery.

This guide provides a detailed template and directive for generating machine-readable, deterministic implementation plans. It is designed for complex software development tasks, such as adding new features, refactoring existing code, or designing system architecture. The output ensures that all steps are atomic, measurable, and structured for autonomous execution by AI agents or human teams, minimizing ambiguity and dependency.

This advanced workflow automates the process of analyzing the operational health of specific Azure resources. It ingests and analyzes raw logs, telemetry data, and performance metrics from diverse Azure services (e.g., VMs, Web Apps, Databases). The system performs comprehensive root cause analysis, classifies issues (Critical, High, Medium), and generates actionable, step-by-step remediation plans, significantly reducing manual troubleshooting time.

This skill analyzes Azure resource groups in depth, identifying all resources, their configurations, and complex interdependencies. It maps relationships—including network connections, data flows, and managed identities—to generate comprehensive, detailed Mermaid architecture diagrams. Use this when you need a clear, structured visualization and full documentation of an Azure environment's topology.

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 comprehensive best practices for creating optimized, secure, and efficient multi-stage Dockerfiles. Learn how to separate build environments (builder stage) from minimal runtime environments, significantly reducing image size and attack surface. Topics covered include layer caching optimization, security hardening (non-root users), and using minimal base images for reproducible builds.

KubeSphere Core acts as the central operating system for cloud-native application management built on Kubernetes. It provides essential functions, including API aggregation, multi-cluster routing, and multi-tenant separation. It manages core resources and simplifies complex cloud-native deployment and operation tasks.

Provides remote access and management for complex, autonomous laboratory protocols executed on Reconfigurable Automation Carts (RACs). Users can submit and manage protocols for cell-free protein expression (validation/optimization), generate fluorescent pixel art, or utilize the EstiMate AI agent for custom workflow feasibility assessments. Covers the entire lifecycle from protocol selection to final sample delivery.

Provides comprehensive guidance on determining the optimal scaling strategy for Qdrant vector databases. Covers when to transition from vertical to horizontal scaling, how to select the right number of nodes and shards, and best practices for setting replication factors to ensure zero-downtime operation and fault tolerance. Also details proper shard management and common scaling pitfalls to avoid.

A comprehensive guide for diagnosing production performance issues in Qdrant vector databases. Learn how to systematically check the optimizer status, analyze high memory usage (OOM crashes), and troubleshoot slow query latencies using key metrics and dedicated API endpoints. Essential for maintaining optimal operational health.