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Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Answer questions about Claude Code / Anthropic agent design patterns (CLAUDE.md layering, skills anatomy, progressive disclosure, memory-first, orchestration, sub-agents, worktree isolation, Karpathy loop). Primary backend is a NotebookLM RAG over Anthropic design notebooks; falls back to bundled references when the notebook is not registered.
INTERNAL ONLY. Forked write-capable variant of the public notebooklm skill. Adds programmatic source ingestion (add_source) for the X/YouTube → NotebookLM pipeline (#119). Headless, non-interactive, cron-driven. NEVER ship publicly.
Documentation harvesting agent for crawling and extracting content from documentation websites. Use for crawling documentation sites and extracting all pages about a subject, building offline knowledge bases from online docs, harvesting API references, tutorials, or guides from documentation portals, creating structured markdown exports from multi-page documentation, and downloading and organizing technical docs for embedding or RAG pipelines. Supports recursive crawling with depth control, content filtering, and structured output.
UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 9 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui). Actions: plan, build, cr...
Generates images via Openrouter API using AI image models. Supports two modes: test (cheap model for iteration) and production (high-quality model for final output). Handles prompt construction, API calls, base64 decoding, and file saving. Supports reference images (logos, mascots) for brand-consistent generation.
Platform-adaptive plugin and extension auto-discovery. Detects the runtime environment (Claude Code, Gemini, Opencode, Kiro) and recommends or installs relevant plugins, extensions, MCP servers, and marketplace integrations. Use when setting up a project, onboarding, or when the user asks about available tools/plugins.
| name | architecture-patterns |
| description | Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems. |
| risk | none |
| source | community |
| date_added | 2026-02-27 |
Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.
Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.
Works well with: event-sourcing-architect, saga-orchestration, workflow-automation, dbos-*
resources/implementation-playbook.md for detailed patterns, checklists, and templates.Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Retrieve prior Architecture Decision Records (ADRs), trade-off analyses, and system design rationale. Critical for maintaining consistency across long-running projects.
# Check for prior architecture/design context before starting
python3 execution/memory_manager.py auto --query "architecture decisions and trade-off analysis for Architecture Patterns"
After completing work, store architecture/design decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Architecture: event-driven microservices with CQRS, Pulsar for messaging, Qdrant for semantic search" \
--type decision --project <project> \
--tags architecture-patterns architecture
Broadcast architecture decisions to ALL agents so implementation stays aligned with the chosen patterns.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Completed architecture review — ADR documented, trade-offs analyzed, team aligned" \
--project <project>
Register architecture tasks in the Control Tower so all agents across machines know the current system design and constraints.