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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

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

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

Improve code through repeated experiments against a measurable target, such as execution time or memory use. After defining scope and constraints, the agent tests changes, keeps improvements, reverts unsuccessful attempts, and logs results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reduce execution time through measured code experiments
• Optimize memory use or bundle size
• Compare changes using benchmark results

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

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

Create and fix Adobe Illustrator automation scripts using ExtendScript, Adobe’s JavaScript-based scripting language. Automate changes to artwork, text, layers, and artboards, process multiple files, and prepare exports such as PDF, SVG, and PNG.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automate repeated changes to artwork and text
• Export multiple Illustrator files
• Generate artwork variations from data

Set up a Python project with AWS CDK, a toolkit for defining cloud infrastructure in code. Follow guidance on prerequisites, AWS credentials, virtual environments, dependencies, template generation, deployment preparation, and reviewing changes before deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a new Python project for AWS CDK
• Preparing a virtual environment and dependencies
• Generating templates and reviewing deployment changes

Develop and troubleshoot distributed applications with Aspire. Coordinate services written in different languages through AppHost, configure connections and startup dependencies, inspect logs and metrics, and prepare deployment settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating an Aspire application from a template
• Connecting services written in different languages
• Diagnosing services through logs and traces

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

Create shareable links that open specific Arize resources, including traces, datasets, and evaluators. Use IDs from logs or exported data, with suitable time ranges, to direct teammates to the relevant view.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Share a trace with teammates
• Open an Arize view from IDs in logs
• Link directly to a dataset or evaluator

Analyze AWS infrastructure definitions, deployed resources, usage metrics, and actual costs to identify savings opportunities. Rank recommendations by savings, risk, and effort, then create individual GitHub issues and a coordinating issue after approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identify underused AWS resources
• Estimate monthly savings from configuration changes
• Prioritize cost reductions by risk and implementation effort

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

Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing a repository’s readiness for AI agents
• Prioritizing improvements from assessment results
• Reviewing scores under an organization’s own policy

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

Add controls to AI agents that use APIs, databases, and files. Use Python patterns for tool permissions, approval requirements, call limits, suspicious input checks, audit logs, and trust scores that guide delegation between agents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Restrict which tools an agent can call
• Check suspicious inputs before execution
• Require human approval for sensitive operations

Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Get oriented in an unfamiliar codebase
• Prepare documentation for a project handover
• Compare documented architecture with implementation

Manage the credentials Arize uses to call LLM providers such as OpenAI and Anthropic. Create, inspect, update, or delete connections, configure allowed models and authentication methods, and retrieve connection IDs for use in evaluations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect an LLM provider to Arize
• Review existing connections and allowed models
• Rotate stored provider API keys

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

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

Coordinate a small AI development team with planning, implementation, and optional independent testing roles. Adapt the process to the work’s complexity, prepare pull requests, and preserve decisions and next steps so later sessions can continue.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting development or taking over an existing project
• Coordinating implementation and independent testing
• Comparing perspectives on product or architecture decisions

Check security audit findings against code evidence and trace how untrusted input moves through a system. Guide a second review, document verification gaps, and assess draft reports against a minimum quality score before delivery.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verifying evidence behind security review findings
• Rechecking input flows after an initial analysis
• Assessing report quality before delivery

Diagnose Arch Linux problems by reviewing recent updates, service logs, and packages. Get a step-by-step investigation plan, ready-to-copy repair commands, verification steps, and guidance for rollback or cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating problems after an Arch Linux update
• Diagnosing services that fail to start
• Planning repairs with verification and rollback steps

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