// Use this skill when documenting significant architectural decisions. Provides ADR templates following the Nygard format with sections for context, decision, consequences, and alternatives. Helps teams maintain architectural memory and rationale for backend systems, API designs, database choices, and infrastructure decisions.
Use this skill when documenting significant architectural decisions. Provides ADR templates following the Nygard format with sections for context, decision, consequences, and alternatives. Helps teams maintain architectural memory and rationale for backend systems, API designs, database choices, and infrastructure decisions.
Architecture Decision Records (ADRs) are lightweight documents that capture important architectural decisions along with their context and consequences. This skill provides templates, examples, and best practices for creating and maintaining ADRs in your projects.
When to use this skill:
Making significant technology choices (databases, frameworks, cloud providers)
Designing system architecture or major components
Establishing patterns or conventions for the team
Evaluating trade-offs between multiple approaches
Documenting decisions that will impact future development
Why ADRs Matter
ADRs serve as architectural memory for your team:
Context Preservation: Capture why decisions were made, not just what was decided
Onboarding: Help new team members understand architectural rationale
Prevent Revisiting: Avoid endless debates about settled decisions
Track Evolution: See how architecture evolved over time
Accountability: Clear ownership and decision timeline
Superseded: Replaced by a later decision (reference ADR number)
Deprecated: No longer recommended but not yet replaced
Rejected: Considered but not adopted (document why)
3. Context
What to include:
Problem statement or opportunity
Business/technical constraints
Stakeholder requirements
Current state of the system
Forces at play (conflicting concerns)
Example:
## Context
Our monolithic application is experiencing scalability issues:
- Database connection pool exhausted during peak traffic
- Deployment of any feature requires full application restart
- Teams blocked waiting for shared resources
- 45-minute build times impacting developer productivity
Business requirements:
- Support 10x traffic growth over next 12 months
- Enable independent team deployments
- Improve time-to-market for new features
Technical constraints:
- Team familiar with Node.js and Python
- AWS infrastructure already in place
- Budget for 2 senior devops engineers
4. Decision
What to include:
The choice being made
Key principles or patterns to follow
What will change as a result
Who is responsible for implementation
Be specific and actionable:
✅ "We will adopt microservices architecture using Node.js with Express"
❌ "We will consider using microservices"
Example:
## Decision
We will migrate from our monolithic architecture to microservices using:
**Technology Stack:**- Node.js 20+ with Express for service implementation
- PostgreSQL for transactional data (per service)
- Redis for caching and session management
- RabbitMQ for async communication between services
- Docker + Kubernetes for deployment orchestration
**Service Boundaries:**- User Service: Authentication, profiles, preferences
- Order Service: Order processing, payment integration
- Inventory Service: Product catalog, stock management
- Notification Service: Email, SMS, push notifications
**Migration Strategy:**- Strangler Fig pattern: Gradually extract services from monolith
- Start with Notification Service (lowest risk, clear boundaries)
- Complete migration within 6 months (Q1-Q2 2026)
**Responsibility:**- Backend Architect: Service design and API contracts
- DevOps Team: Kubernetes setup and deployment pipelines
- Team Leads: Migration execution per service
5. Consequences
What to include:
Positive outcomes (benefits)
Negative outcomes (costs, risks, trade-offs)
Neutral outcomes (things that change but aren't clearly better/worse)
Be honest about trade-offs:
## Consequences### Positive-**Scalability**: Each service can scale independently based on load
-**Development Velocity**: Teams can deploy services without coordination
-**Technology Freedom**: Services can use different tech stacks if needed
-**Fault Isolation**: Failure in one service doesn't crash entire system
-**Faster Build Times**: Services build in 2-5 minutes vs 45 minutes
### Negative-**Operational Complexity**: Managing 4+ services vs 1 application
-**Network Latency**: Inter-service calls add 10-50ms per hop
-**Distributed Debugging**: Harder to trace requests across services
-**Data Consistency**: Eventually consistent vs immediate consistency
-**Learning Curve**: Team needs to learn Kubernetes, service mesh concepts
-**Initial Slowdown**: 2-3 months of infrastructure setup before benefits
### Neutral-**Testing Strategy**: Shift from integration tests to contract tests
-**Monitoring**: Need distributed tracing (Jaeger) vs simple logs
-**Cost**: Higher infrastructure costs offset by improved developer productivity
6. Alternatives Considered
Document at least 2 alternatives:
For each alternative, explain:
What it was
Why it was considered
Why it was not chosen
Example:
## Alternatives Considered### Alternative 1: Optimize Existing Monolith**Description:**- Add read replicas for database
- Implement caching layer (Redis)
- Use horizontal scaling with load balancer
**Pros:**- Lower complexity, team already familiar
- Faster implementation (4-6 weeks)
- No architectural re-work needed
**Cons:**- Doesn't solve deployment coupling
- Limited scalability ceiling
- Build times remain slow
- Teams still blocked on shared resources
**Why not chosen:**
This addresses symptoms but not root causes. We'd face the same issues again in 12-18 months as we continue growing.
### Alternative 2: Serverless Architecture (AWS Lambda)**Description:**- Break application into Lambda functions
- Use API Gateway for routing
- DynamoDB for storage
**Pros:**- Extreme scalability
- Pay-per-use pricing model
- No server management
**Cons:**- Vendor lock-in to AWS
- Cold start latency (500ms+)
- Limited to 15-minute execution time
- Team has no serverless experience
- Harder to debug and test locally
**Why not chosen:**
Risk too high given team inexperience. Cold starts unacceptable for our real-time features. Microservices provide similar benefits with more control.
