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.
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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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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.
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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.
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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.
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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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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.
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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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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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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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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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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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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.
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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.
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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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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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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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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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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.
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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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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.
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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.
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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.
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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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