test-reporting-analytics
Advanced QE reporting, quality dashboards, and predictive analytics for test metrics, code coverage, and deployment readiness to drive data-informed quality decisions.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
515 skills found
Advanced QE reporting, quality dashboards, and predictive analytics for test metrics, code coverage, and deployment readiness to drive data-informed quality decisions.
Analyze markdown documentation files to ensure compliance with predefined AI token budgets and optimize content for efficient AI ingestion.
Xcode 26 expert for Liquid Glass, Foundation Models, and Apple Intelligence framework updates across SwiftUI, UIKit, AppKit, and more.
Autonomous pattern detection and skill recommendation engine that monitors project memory, logs, and task lists to evolve your AI agent's capabilities automatically.
Enables cross-session context persistence for Claude Code, managing history, project decisions, and workflow patterns to ensure seamless task continuation.
A universal skill for automating GitHub Project V2 Kanban boards, supporting status transitions, sprint management, and interactive workflows via CLI.
Official Mastra framework guide. Master AI agent and workflow development with local documentation lookup, API verification, and TypeScript-based project management.
Generate hierarchical, token-efficient AGENTS.md files for AI coding agents to provide repository-wide context and project-specific guidelines.
Convert diverse file formats like PDFs, Office docs, images, audio, and web content into clean Markdown, specifically optimized for LLM ingestion, RAG pipelines, and automated text analysis workflows.
Neural web search and code context retrieval via Exa AI. Ideal for documentation, technical research, code examples, and company intelligence.
Maintain Mintlify documentation sites: configure navigation, manage MDX content, add components, and handle API references.
Comprehensive AI-generated text detection framework. Features multi-layer analysis of vocabulary, structural patterns, model-specific fingerprints, and technical metadata artifacts to identify AI authorship.