Skill UI

Automated 7-stage workflow for auditing, cleaning, reviewing, and benchmarking GitHub repositories, turning messy projects into polished, production-ready codebases with guided analysis, manual reflection, and final validation.

Automates the pre-development workflow for large-scale transformations by performing project analysis, task decomposition, documentation generation, progress tracking, and sub-skill creation before any code is written.

This comprehensive guide provides detailed procedural steps for configuring and executing CodeQL code scanning. It covers both implementing automated workflows using GitHub Actions (`codeql.yml`) and running scans via the CodeQL CLI. Users will learn how to manage language matrices, set build modes, interpret SARIF outputs, handle monorepo structures, and troubleshoot complex security analysis setups across multiple languages (e.g., Python, Java, C++).

A comprehensive workflow designed for Product Managers and technical leads to systematically identify, prioritize, and document missing features in a software project. It guides users through project understanding, gap analysis, applying a scoring matrix for prioritization, developing detailed Minimum Viable Product (MVP) specifications, and structuring the output as actionable development issues (e.g., GitHub Issues) with dependency mapping and workload optimization.

A comprehensive guide to creating production-grade Product Requirements Documents (PRDs). This skill helps bridge the gap between vague business concepts and concrete technical specifications for modern software systems and AI features. It enforces a structured workflow, ensuring all necessary components—from user stories and success KPIs to technical architecture and risk analysis—are defined.

This skill provides comprehensive command-line interface (CLI) functionality for managing the entire lifecycle of experiments within the Arize platform. Users can list, retrieve metadata, create new experiments from various data files (CSV, JSON, Parquet), and export detailed run results for deep analysis. It supports linking experiments to specific dataset versions, making it essential for structured ML model evaluation and performance comparison workflows.

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.

Phoenix Evals lets teams build evaluators for AI/LLM apps by wiring deterministic code with LLM nuance, running Python or TypeScript workloads, validating against humans, and iterating through error analysis, RAG, and production workflows.

Generates detailed prompts to document end-to-end application workflows by auto-detecting architectures, stacks, entry points, services, data access, error handling, and tests across .NET, Java/Spring, React, microservices, and other tech stacks.