HakjunMIN

Implement one Azure-backed strangler slice in the target repository after architecture, requirements, Azure review, rollback plan, and touched-seam harnesses are green. Produces tested, observable, reversible code and deployment evidence.

Use before any destructive refactor or UX/backend change to capture the legacy system's current behavior as characterization tests and golden-master fixtures — the safety net that catches regressions. This is the harness gate; it must be green before modernization code begins.

Implement a vetted legacy-web UX slice in the target repository after requirements and touched-seam harnesses are green. Discovers the real frontend stack, changes code test-first, and records implementation evidence before browser validation and review.

Use when starting or resuming a legacy web modernization (UI/UX and/or backend) — routes between renovation phases, tracks state in plan.md, and enforces the harness/test/review gates. Entry point for the harness-driven-renovate workflow.

Use to modernize a legacy backend incrementally using the strangler-fig pattern — route slices of traffic to new event-driven / cloud-native services behind stable seams while the characterization harness guards against regressions. Requires the harness gate to be green.

Use as a mandatory gate before any renovation change is considered done or merged — rigorous review focused on harness regressions, legacy parity, security, and readability. Cross-cutting; invoke on every change. Composes with requesting-code-review / code-review agent.

Use whenever you write new or refactored code during a renovation (frontend or backend) — author tests first (red-green-refactor) and build the automated test coverage the modernized system needs. Cross-cutting; invoke on every code-producing step. Composes with the tdd skill.

Use to modernize a legacy web UI/UX — collapse scroll/popup-heavy, multi-screen flows into a single coherent flow, remove accumulated dead features, and design a realtime operations dashboard. Requires the harness gate to be green before changing behavior.

Evaluate agent question/answer behavior with Azure AI Evaluation SDK for Python. Use when: azure-ai-evaluation, Azure AI Evaluation, agent eval, Q/A evaluation, RAG eval, grounded QA evaluation, relevance, groundedness, coherence, fluency, similarity, safety evaluators, simulator, 질문 답변 평가.

Implement, review, migrate, or troubleshoot Microsoft Agent Framework Python observability with OpenTelemetry, traces, logs, metrics, Azure Monitor Application Insights, Aspire Dashboard, console exporters, MCP trace propagation, configure_otel_providers, enable_instrumentation, and GenAI semantic conventions.

Create, update, refactor, explain, or review Microsoft Agent Framework solutions for Python.

Use for Azure AI in Python: Search, Speech, OpenAI, and Document Intelligence. Helps with search, vector or hybrid search, speech-to-text, text-to-speech, transcription, and OCR using Python-first guidance.

Python 프로젝트 의존성 관리 및 검토 가이드

Python 프로젝트 아키텍처 분석 및 설계 검토 가이드

Python 코드 스타일 검사 및 품질 관리 가이드

Python 프로젝트 보안 취약점 스캔 및 보안 강화 가이드

Rewrite vanilla Kubernetes manifests or Helm charts toward AKS best practices. Use for: migrate manifests to aks, migrate helm chart to aks, convert loadbalancer to aks ingress, add workload identity, harden kubernetes for aks.

Assess a vanilla Kubernetes manifest set or Helm chart for migration to Azure Kubernetes Service. Use for: assess k8s for aks, assess helm chart for aks, migration readiness for aks, brownfield kubernetes to aks, aks target baseline, aks migration report.

Validate AKS migration output for Kubernetes manifests and Helm charts. Use for: test aks migration, validate migrated manifests, validate migrated helm chart, golden fixture regression for aks migration.

Use when executing implementation plans with independent tasks in the current session