Skill UI

FAF Expert is an advanced tool for creating standardized, persistent context for complex software projects. It uses the IANA-registered FAF format to transform codebases into AI-intelligent assets. Use it for multi-AI workflow management (Claude, Gemini, etc.), enterprise-grade deployment, and standardizing project context across development lifecycles.

This tool helps developers run comprehensive test suites using pytest and generates detailed code coverage reports. It identifies specific lines of code that have not been covered by tests, guiding developers to improve test quality until 100% coverage is achieved. Use it to ensure robust and reliable software.

Acts as a senior software engineer to meticulously review and refactor entire codebases. This skill ensures that the code is clean, highly maintainable, and strictly adheres to defined coding standards and best practices specified across multiple project instruction files. It guarantees the integrity of the existing file structure and ensures all unit tests remain passing after modifications.

This advanced agent acts as a highly constrained implementation expert, designed to execute complex, multi-step plans with absolute fidelity. It strictly adheres to the provided workflow, making only specified changes, updating the plan status in real-time, and running tests until completion. Ideal for disciplined software development, detailed project execution, or formal process adherence.

A comprehensive tool that analyzes complex codebases to generate detailed, structured architectural blueprints. It automatically detects and documents the entire technology stack, including primary languages (.NET, Java, Python, etc.), dependencies, versions, licensing, and implementation patterns. The output provides actionable documentation covering coding conventions, usage examples (API, data access), and visual architecture maps, ensuring consistency across guided software development projects.

This skill provides a structured approach to debugging software issues by prioritizing root cause analysis over quick fixes. It guides users through four phases: investigation, pattern analysis, hypothesis verification, and implementation. Essential for resolving test failures, production bugs, and performance issues efficiently, preventing recurring problems and architectural debt.

A comprehensive guide to setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines within KubeSphere. This tool integrates with Jenkins, supports both graphical editing and Jenkinsfile-based workflows, and enables automated deployments across multi-cluster environments using ArgoCD for GitOps management. Ideal for managing the full software delivery lifecycle.

Generate comprehensive software requirements specifications following EARS format. Analyzes project context, creates acceptance criteria, and ensures scope boundaries are clearly defined before implementation. Supports both greenfield and brownfield projects with sub-agent delegation for codebase exploration and domain research.

This assistant serves as a hands-on implementation partner, guiding users to build functional tools, scripts, dashboards, and prototypes. It emphasizes rapid iteration, showing working code, and applying proven development patterns such as data-to-deck pipelines, HTML presentations, and structured build practices. Use it when the goal is to transform analysis or concepts into tangible, working software or robust automation.

This skill helps users design robust and optimal software interfaces (APIs) for a module by applying the 'Design It Twice' philosophy. It generates multiple, radically different architectural designs using parallel agents, allowing for comprehensive comparison of trade-offs (e.g., simplicity vs. flexibility, specialized vs. general-purpose). Use this when you need to explore various structural options or compare module shapes before committing to a final design.

A guide to building resilient software integrations when dealing with external APIs that lack formal versioning, changelogs, or guaranteed backward compatibility. Learn defensive coding patterns, implementing schema drift detection via CLI tools (curl, jq), using fallback field names, and executing gradual feature flag rollouts to maintain production stability.

This internal agent skill defines a systematic, multi-step procedure for scanning GitHub repositories to gather comprehensive evidence supporting or explaining bug clusters. The process covers searching open issues, inspecting recent commits, analyzing code paths, and checking deployment history. It assigns evidence confidence tiers (Exact, Strong, Moderate, Weak) to provide a deep, actionable understanding of potential software defects.