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.
Investigate AWS production incidents using reusable CloudWatch log queries and metric calculations. Narrow affected resources, compare alarms with deployment events, and reconstruct a timeline to identify changes that may explain the symptoms.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Organize human review in Arize by defining label choices, numeric ranges, or free-text feedback fields. Manage review queues and reviewers, submit annotations for individual records, and apply existing labels to project spans, or individual processing records, in bulk.
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.
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.
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.
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.
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.
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.
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.
Assess an AWS resource using CloudWatch logs and metrics, the measurements that describe its behavior. Investigate failures and performance issues, classify their severity, and produce a remediation report with staged actions, CLI commands, and monitoring recommendations.
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.
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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