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Design an AI agent around its requirements, or audit an existing agent for architectural problems. Deliver justified decisions, verification plans, and a PDF report with a diagram, covering budgets, permissions, stopping rules, and failure handling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design an agent from an idea or specification
• Audit an existing agent’s architecture
• Diagnose errors and false completion reports

Find and evaluate a small, compatible set of AI Agent Skills for a multi-step goal. Check existing capabilities, compare candidates for fit and safety, identify overlaps, and prepare recommendations with installation subject to user approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a set of Skills for a multi-step project
• Audit installed Skills for overlap and conflicts
• Find supporting Skills for writing quality or compliance

Check Bicep infrastructure definitions before deploying to Azure. Validate syntax, preview resource changes, check deployment permissions, and produce a Markdown report of issues and recommendations using Azure CLI or Azure Developer CLI workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validating Bicep files before deployment
• Previewing resource creation, modification, and deletion
• Checking deployment permissions

Create and run evaluations in Arize using an LLM as the judge. Define scoring criteria, connect them to actual data fields, evaluate recorded operations or experiment results, and set up continuous scoring for new data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check answers for unsupported claims
• Score experiment results for correctness
• Continuously evaluate new production records

Get instructions for installing and reviewing the full ai-ready skill in Copilot CLI. This wrapper explains how to add the latest version, reload skills, and request repository preparation for AI contributions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Install the full skill in Copilot CLI
• Review the skill before loading it
• Update an existing skill installation

Review AI prompts for harmful output risks, bias, privacy concerns, security weaknesses, and clarity. Receive an assessment, a revised prompt, and suggested tests with explanations of the principles behind each improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a prompt before putting it into use
• Check wording for stereotypes and bias
• Assess data exposure and prompt injection risks

Retrieve current Azure retail prices and estimate monthly or annual costs by service, region, and configuration. Compare payment options and calculate Copilot Studio credit usage and estimated costs from expected agent activity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate monthly and annual Azure costs
• Compare prices across regions and configurations
• Compare reservations, savings plans, and spot pricing

Build ASP.NET Minimal API endpoints with explicit request and response types, input validation, and standard error responses. Organize related endpoints and add OpenAPI documentation, a structured description of API operations, parameters, and schemas.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building Minimal API endpoints with explicit types
• Adding operation and parameter descriptions to OpenAPI
• Organizing related endpoints with MapGroup()

Check AI agent plugins and tools for changed, missing, or untracked files using SHA-256 file fingerprints. Generate integrity records, review dependency version settings, and check requirements before promoting a plugin to production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate integrity records for plugin files
• Check third-party tools for file changes
• Audit dependency version settings

Create, review, migrate, or troubleshoot Azure Developer CLI projects. Align application settings, infrastructure definitions, deployment environments, secrets, and automated delivery workflows while checking that configuration files and deployment requirements agree.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating an Azure Developer CLI project structure
• Reviewing azure.yaml against Bicep or Terraform definitions
• Organizing environment settings and secret handling

Design Azure infrastructure from plain language or visualize existing resources as interactive diagrams. Refine the architecture through conversation, then generate Bicep infrastructure code, review it, and validate changes before deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a new Azure architecture
• Visualizing existing Azure resources
• Planning cost or security changes

Review AWS infrastructure code and deployed resources against the six Well-Architected pillars. Produce risk-ranked findings, remediation examples, and an architecture diagram, then create GitHub issues for tracking improvements after user confirmation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review AWS infrastructure code and deployed resources
• Prioritize findings by risk
• Prepare infrastructure code remediation examples

Ask plain-language questions about AWS resources, settings, and costs, and receive readable tables using read-only AWS CLI commands. Inspect services and filter results by region, state, or tags without creating, changing, or deleting resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Listing running EC2 instances
• Checking S3 encryption and public access settings
• Finding unattached EBS volumes

Review an iOS app’s code, settings, and submission materials for App Store rejection risks. Receive prioritized findings with supporting evidence, concrete fixes, verification steps, and draft notes to help reviewers test the app.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check an iOS app before App Store submission
• Review privacy, permissions, and in-app purchases
• Investigate rejection risks in login and user content flows

Analyze Azure infrastructure definitions, deployed resources, usage metrics, and pricing to identify cost reductions. Rank recommendations by savings, risk, and effort, then create individual GitHub issues and a coordinating EPIC issue after user approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding Azure resources with oversized service tiers
• Comparing storage and database cost reduction options
• Prioritizing savings by risk and implementation effort

Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add beginner-friendly explanations to existing code
• Document the reasoning behind syntax and design choices
• Adjust comment depth for readers with different experience

Analyze a codebase to document its technologies, architecture, dependencies, and implementation patterns. Produce a Markdown reference with optional diagrams, code examples, and guidance for adding features consistently with the existing design.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understand an unfamiliar codebase's architecture
• Document components, dependencies, and data flow
• Create guidance for adding features consistently

Plan and implement connections between Arduino devices and Azure IoT Hub, directly or through an IoT Edge gateway. Define sensor messages, device credentials, offline buffering, remote commands, and implementation tasks for firmware, gateways, and cloud services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect Arduino sensors to Azure
• Design a local gateway for field devices
• Buffer sensor data during network outages

Create and manage Arize datasets for evaluation and experiments using the ax CLI. Import records from files, append examples, inspect dataset versions, and export a chosen version for offline analysis, with checks for field consistency and export completeness.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating evaluation datasets from files
• Adding examples to an existing test set
• Inspecting dataset metadata and versions

Create and analyze AI model evaluation experiments in Arize using the ax CLI, a command-line tool. Collect real model outputs, export results, compare evaluation scores, and identify examples where a new model performs worse.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing models on the same evaluation dataset
• Finding examples that perform worse after a model change
• Exporting experiment results for further analysis

Design, refine, or review web and iOS interfaces using the product’s existing components and visual style. Consult relevant screen examples when useful, then inspect the result for layout problems, inaccessible controls, and broken interactions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing screens that fit an existing product
• Refining a dashboard or simplifying an interface
• Reviewing layouts and interactions before release

Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up tracing for an LLM application
• Recording tool inputs and outputs in an agent
• Adding Arize export to an existing OpenTelemetry setup

Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identify ads and keywords to pause
• Find campaigns and audiences worth scaling
• Assess whether A/B test results are meaningful

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results