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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.

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• Create project instructions for VS Code Copilot
• Refresh agent rules after project changes
• Split monorepo instructions by project area

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.

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

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.

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

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.

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

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.

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

Review an AI agent codebase against ten OWASP ASI security risk areas. Examine tool permissions, input validation, identity checks, audit logs, and other controls, then summarize coverage, gaps, and recommended improvements in a report.

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• Reviewing agent security before production deployment
• Mapping existing controls to ten security risk areas
• Preparing findings for a security review or audit

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.

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

Compare C# Web API source code with its TypeScript or JavaScript clients to find mismatched data fields, required inputs, and response handling. Receive an audit report showing client impact, severity, and confidence in each match.

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• Check client impact before merging a DTO or controller change
• Audit API compatibility before a release
• Find stale request fields and missing response fields

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.

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

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.

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

Inspect existing LLM application records in Arize to investigate errors, slow operations, or behavior changes. Export traces, which group related operations, or individual operations and conversation sessions as JSON using the ax command-line tool.

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• Investigate errors in an LLM application
• Review the inputs and outputs of a conversation
• Find slow operations in recorded traces

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.

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

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.

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

Manage Azure Container Registry, a service for storing container images, with Azure CLI. Build images in the cloud, copy them between registries, clean up stored artifacts, and configure authentication and network access.

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• Build container images in Azure without local Docker
• Copy images from another registry
• Clean up unused images and tags

Manage Azure DevOps projects, repositories, pull requests, pipelines, and work items through command-line tools. The skill guides authentication, resource operations, and automation scripts, with references for builds, permissions, packages, and common errors.

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• Create and review pull requests
• Run pipelines and check builds
• Manage work items and sprints

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.

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• 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.

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

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.

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

Set up telemetry, data about application operation, for web apps hosted in Azure. Choose automatic or manual instrumentation, create an Azure App Insights resource, and configure application code to send monitoring data.

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• Add telemetry to an ASP.NET Core app hosted in Azure
• Send operational data from a Node.js app to Azure App Insights
• Configure automatic instrumentation for Azure App Service

Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.

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• Set shared readiness standards across an organization
• Focus assessments on AI tooling
• Adjust the importance of individual checks

Turn a screen recording of repetitive work into automation scripts. The skill reconstructs the workflow, proposes options suited to installed tools, and helps build and test the chosen approach while preserving human review steps.

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• Automating repeated report downloads and spreadsheet filtering
• Turning file sorting and renaming into scripts
• Reducing repetitive transfers between applications

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.

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

Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.

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• Improve a prompt using failed production traces
• Fix responses that use the wrong output format
• Review unsupported answers in retrieval-based responses

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.

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