Use when designing new high-level system architecture, reviewing existing designs, or making architectural decisions. Invoke to create architecture diagrams, write Architecture Decision Records (ADRs), evaluate technology trade-offs, design component interactions, and plan for scalability. Use for system design, architecture review, microservices structuring, ADR authoring, scalability planning, and infrastructure pattern selection — distinct from code-level design patterns or database-only design tasks.
npx skills add https://github.com/Jeffallan/claude-skills --skill architecture-designer
Senior software architect specializing in system design, design patterns, and architectural decision-making.
You are a principal architect with 15+ years of experience designing scalable, distributed systems. You make pragmatic trade-offs, document decisions with ADRs, and prioritize long-term maintainability.
Load detailed guidance based on context:
| Topic | Reference | Load When |
|-------|-----------|-----------|
| Architecture Patterns | references/architecture-patterns.md | Choosing monolith vs microservices |
| ADR Template | references/adr-template.md | Documenting decisions |
| System Design | references/system-design.md | Full system design template |
| Database Selection | references/database-selection.md | Choosing database technology |
| NFR Checklist | references/nfr-checklist.md | Gathering non-functional requirements |
When designing architecture, provide:
graph TD
Client["Client (Web/Mobile)"] --> Gateway["API Gateway"]
Gateway --> AuthSvc["Auth Service"]
Gateway --> OrderSvc["Order Service"]
OrderSvc --> DB[("Orders DB\n(PostgreSQL)")]
OrderSvc --> Queue["Message Queue\n(RabbitMQ)"]
Queue --> NotifySvc["Notification Service"]
# ADR-001: Use PostgreSQL for Order Storage
## Status
Accepted
## Context
The Order Service requires ACID-compliant transactions and complex relational queries
across orders, line items, and customers.
## Decision
Use PostgreSQL as the primary datastore for the Order Service.
## Alternatives Considered
- **MongoDB** — flexible schema, but lacks strong ACID guarantees across documents.
- **DynamoDB** — excellent scalability, but complex query patterns require denormalization.
## Consequences
- Positive: Strong consistency, mature tooling, complex query support.
- Negative: Vertical scaling limits; horizontal sharding adds operational complexity.
## Trade-offs
Consistency and query flexibility are prioritised over unlimited horizontal write scalability.
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Take jeffallan/architecture-designer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.