Azure/agent-landing-zone

engineering-principles

Agent Landing Zone architecture and implementation principles. Use for design, review, meaningful refactoring, Azure integration, security, testing, or operational changes.

원문 언어: 영어

architecture-decision

Conducts and records a verifiable Agent Landing Zone architectural decision. Use when a choice alters repositories, boundaries, contracts, identity, data, deployment topology, or operation with meaningful reversal cost.

원문 언어: 영어

documentation-consistency

Keeps Agent Landing Zone user and operator documentation aligned with shipped behavior. Use for features, configuration keys, deployment parameters, defaults, component pins, operations, or breaking changes.

원문 언어: 영어

multi-repo-release

Prepares and validates Agent Landing Zone umbrella and multi-repository releases. Use whenever work involves release preparation, semantic versions, release branches, manifest or component pins, changelog release entries, tags, GitHub Release notes, or AI…

원문 언어: 영어

architecture-decision

Conducts and records a verifiable AI Landing Zone architecture decision. Use when a choice alters module boundaries, contracts, identity, networking, deployment topology, or operation with meaningful reversal cost.

원문 언어: 영어

deployment-operations

Safely investigates and operates AI Landing Zone preflight, What-If, azd, Azure DevOps, and jumpbox bootstrap paths. Use for approved deployment operations or incident diagnosis.

원문 언어: 영어

documentation-consistency

Keeps AI Landing Zone contributor, user, and operator documentation aligned with shipped Bicep behavior. Use for parameters, defaults, outputs, topology, deployment flow, operations, or releases.

원문 언어: 영어

engineering-principles

AI Landing Zone architecture and implementation principles. Use before Azure or Bicep design, review, meaningful refactoring, security, validation, release, or operational work.

원문 언어: 영어

iac-validation

Builds an evidence-based validation plan for AI Landing Zone Copilot assets, Bicep, parameters, scripts, and Azure deployment behavior. Use after implementation or during regression investigation.

원문 언어: 영어

release-management

Prepares and validates AI Landing Zone semantic releases. Use for changelog entries, manifest versions, release branches, tags, GitHub Releases, and post-release branch reconciliation.

원문 언어: 영어

terraform-parity-proposal

Converts an approved structured parity handoff into a read-only, human-reviewable proposal plan for the allow-listed Terraform repository.

원문 언어: 영어

speckit-analyze

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.

원문 언어: 영어

speckit-checklist

Generate a custom checklist for the current feature based on user requirements.

원문 언어: 영어

speckit-clarify

Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.

원문 언어: 영어

speckit-constitution

Create or update the project constitution from interactive or provided principle inputs.

원문 언어: 영어

speckit-converge

Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.

원문 언어: 영어

speckit-implement

Execute the implementation plan by processing and executing all tasks defined in tasks.md

원문 언어: 영어

speckit-plan

Execute the implementation planning workflow using the plan template to generate design artifacts.

원문 언어: 영어

speckit-specify

Create or update the feature specification from a natural language feature description.

원문 언어: 영어

speckit-tasks

Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.

원문 언어: 영어

speckit-taskstoissues

Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.

원문 언어: 영어