dataops-platform

JetBrains MCP 도구를 활용한 빠른 개발 워크플로우. IDE 검사, Run Configuration 실행, 리팩토링, 파일 검색 등을 통해 Gradle 의존도를 줄이고 개발 속도를 10배 향상시킵니다. Kotlin/Spring 개발 시 필수로 사용하세요.

Kotlin testing with JUnit 5, MockK, Spring test slices, and fast feedback commands. Provides single test execution, incremental builds, and JetBrains MCP integration for rapid TDD cycles. Use when writing tests for Kotlin/Spring code, running specific tests, or debugging test failures.

Kotlin testing with JUnit 5, MockK, Spring test slices, and Testcontainers. Provides mocking patterns, test slice selection, and TDD workflow for Spring Boot + Kotlin. Use when writing tests for Kotlin/Spring code, setting up test infrastructure, or debugging test failures.

System architecture analysis and design. Validates architecture patterns, generates dependency graphs, and evaluates module boundaries. Use when analyzing structure, checking layer compliance, or making design decisions.

Token-efficient codebase exploration using MCP-first approach. Locates functions, classes, patterns, and traces dependencies with 80-90% token savings. Use when searching code, finding implementations, or tracing call chains.

Hypothesis-driven debugging with systematic investigation. Identifies root causes, creates minimal reproductions, and implements fixes. Use when investigating errors, bugs, crashes, or unexpected behavior.

Token-efficient codebase exploration using MCP servers (Serena, Context7, JetBrains, Claude-mem). Reduces token usage by 80-90% through structured queries. Use ALWAYS before reading files to minimize context window usage.

Safe code refactoring with test protection and incremental changes. Improves structure without changing behavior. Use when extracting methods, reducing duplication, or restructuring code.

Token-efficient documentation search using Serena Document Index. 90%+ token savings vs reading full files. Use BEFORE reading README.md or docs/ files. Triggers on architecture questions, pattern lookups, and project-specific documentation needs.

Hypothesis-driven debugging with systematic investigation. Identifies root causes, creates minimal reproductions, and implements fixes. Use when investigating errors, bugs, crashes, or unexpected behavior.

Performance optimization and bottleneck detection. Identifies N+1 queries, memory leaks, async issues, and caching opportunities. Use when investigating slow operations, optimizing response times, or detecting performance issues.

Safe code refactoring with test protection and incremental changes. Improves structure without changing behavior. Use when extracting methods, reducing duplication, or restructuring code.

Analyzes test directory structure, coverage gaps, and helper consolidation opportunities. Produces coverage reports and refactoring recommendations. Use when auditing test suites, planning test improvements, or identifying coverage gaps.

Token-efficient context gathering and synthesis from multiple sources (memory, docs, web). Orchestrates MCP tools to build comprehensive context before analysis or interviews. Use when starting discovery, research, or analysis tasks.

Stakeholder interview and requirements elicitation. Discovers hidden needs, surfaces contradictions, and produces structured specifications. Use when gathering requirements, conducting discovery interviews, or writing PRDs/feature specs.

Self-evaluation and quality validation for specifications and documents. Identifies gaps, contradictions, and missing details using structured checklists. Use after drafting specs, PRDs, or feature documents.

Structured cross-review protocol between specialized agents. Ensures scope alignment, priority calibration, and domain-aware feedback. Use when one agent reviews another's work, during handoffs, or when validating cross-cutting concerns.

GitHub Actions CI/CD pipelines with caching, matrix builds, and deployment strategies. Focuses on build speed, reliability, and security. Use when creating or optimizing CI/CD workflows, debugging pipeline failures, or implementing deployment automation.

Pytest fixture design, conftest.py hierarchy, and DRY test code patterns. Identifies duplicate fixtures, plans fixture scope, and designs conftest.py structure. Use when creating test directories, refactoring test fixtures, or reviewing test code for duplication.

React testing with Vitest, React Testing Library, and MSW. Focuses on user-centric testing, component isolation, and API mocking. Use when writing tests for React components, hooks, or debugging test failures in frontend code.

Systematic code review with security, performance, and architecture analysis. Provides actionable fix suggestions and GitHub PR integration. Use when reviewing PRs, validating code changes, or checking code quality.

Technical documentation generation and maintenance. Creates API docs, code comments, READMEs, and changelogs. Use when documenting code, APIs, or creating project documentation.

Git workflow automation including commit messages, PR management, and branch strategies. Handles merge conflicts and maintains clean history. Use when committing, creating PRs, or managing branches.

Comprehensive testing strategies including TDD, unit, integration, E2E, and property-based testing. Ensures code quality and prevents regressions. Use when writing tests, implementing features test-first, or improving coverage.