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Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.
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Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions. 📦 Ships with agentic-awesome-skills
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architecture-decision-records.SKILL.md
---
name : architecture-decision-records
description : "Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions."
risk : unknown
source : community
date_added : "2026-02-27"
---
# Architecture Decision Records
Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.
## Use this skill when
- Making significant architectural decisions
- Documenting technology choices
- Recording design trade-offs
- Onboarding new team members
- Reviewing historical decisions
- Establishing decision-making processes
## Do not use this skill when
- You only need to document small implementation details
- The change is a minor patch or routine maintenance
- There is no architectural decision to capture
## Instructions
1. Capture the decision context, constraints, and drivers.
2. Document considered options with tradeoffs.
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3. Record the decision, rationale, and consequences.
4. Link related ADRs and update status over time.
## Core Concepts
### 1. What is an ADR?
An Architecture Decision Record captures:
- **Context**: Why we needed to make a decision
- **Decision**: What we decided
- **Consequences**: What happens as a result
### 2. When to Write an ADR
| Write ADR | Skip ADR |
|-----------|----------|
| New framework adoption | Minor version upgrades |
| Database technology choice | Bug fixes |
| API design patterns | Implementation details |
| Security architecture | Routine maintenance |
| Integration patterns | Configuration changes |
### 3. ADR Lifecycle
```
Proposed → Accepted → Deprecated → Superseded
↓
Rejected
```
## Templates
### Template 1: Standard ADR (MADR Format)
```markdown
# ADR-0001: Use PostgreSQL as Primary Database
## Status
Accepted
## Context
We need to select a primary database for our new e-commerce platform. The system
will handle:
- ~10,000 concurrent users
- Complex product catalog with hierarchical categories
- Transaction processing for orders and payments
- Full-text search for products
- Geospatial queries for store locator
The team has experience with MySQL, PostgreSQL, and MongoDB. We need ACID
compliance for financial transactions.
## Decision Drivers
* **Must have ACID compliance** for payment processing
* **Must support complex queries** for reporting
* **Should support full-text search** to reduce infrastructure complexity
* **Should have good JSON support** for flexible product attributes
* **Team familiarity** reduces onboarding time
## Considered Options
### Option 1: PostgreSQL
- **Pros**: ACID compliant, excellent JSON support (JSONB), built-in full-text
search, PostGIS for geospatial, team has experience
- **Cons**: Slightly more complex replication setup than MySQL
### Option 2: MySQL
- **Pros**: Very familiar to team, simple replication, large community
- **Cons**: Weaker JSON support, no built-in full-text search (need
Elasticsearch), no geospatial without extensions
### Option 3: MongoDB
- **Pros**: Flexible schema, native JSON, horizontal scaling
- **Cons**: No ACID for multi-document transactions (at decision time),
team has limited experience, requires schema design discipline
## Decision
We will use **PostgreSQL 15** as our primary database.
## Rationale
PostgreSQL provides the best balance of:
1. **ACID compliance** essential for e-commerce transactions
2. **Built-in capabilities** (full-text search, JSONB, PostGIS) reduce
infrastructure complexity
3. **Team familiarity** with SQL databases reduces learning curve
4. **Mature ecosystem** with excellent tooling and community support
The slight complexity in replication is outweighed by the reduction in
additional services (no separate Elasticsearch needed).
## Consequences
### Positive
- Single database handles transactions, search, and geospatial queries
- Reduced operational complexity (fewer services to manage)
- Strong consistency guarantees for financial data
- Team can leverage existing SQL expertise
### Negative
- Need to learn PostgreSQL-specific features (JSONB, full-text search syntax)
- Vertical scaling limits may require read replicas sooner
- Some team members need PostgreSQL-specific training
### Risks
- Full-text search may not scale as well as dedicated search engines
- Mitigation: Design for potential Elasticsearch addition if needed
## Implementation Notes
- Use JSONB for flexible product attributes
- Implement connection pooling with PgBouncer
- Set up streaming replication for read replicas
- Use pg_trgm extension for fuzzy search
## Related Decisions
- ADR-0002: Caching Strategy (Redis) - complements database choice
- ADR-0005: Search Architecture - may supersede if Elasticsearch needed
## References
- [PostgreSQL JSON Documentation](https://www.postgresql.org/docs/current/datatype-json.html)
- [PostgreSQL Full Text Search](https://www.postgresql.org/docs/current/textsearch.html)
- Internal: Performance benchmarks in `/docs/benchmarks/database-comparison.md`
```
### Template 2: Lightweight ADR
```markdown
# ADR-0012: Adopt TypeScript for Frontend Development
**Status**: Accepted
**Date**: 2024-01-15
**Deciders**: @alice, @bob, @charlie
## Context
Our React codebase has grown to 50+ components with increasing bug reports
related to prop type mismatches and undefined errors. PropTypes provide
runtime-only checking.
## Decision
Adopt TypeScript for all new frontend code. Migrate existing code incrementally.
## Consequences
**Good**: Catch type errors at compile time, better IDE support, self-documenting
code.
**Bad**: Learning curve for team, initial slowdown, build complexity increase.
**Mitigations**: TypeScript training sessions, allow gradual adoption with
`allowJs: true`.
```
### Template 3: Y-Statement Format
```markdown
# ADR-0015: API Gateway Selection
In the context of **building a microservices architecture**,
facing **the need for centralized API management, authentication, and rate limiting**,
we decided for **Kong Gateway**
and against **AWS API Gateway and custom Nginx solution**,
to achieve **vendor independence, plugin extensibility, and team familiarity with Lua**,
accepting that **we need to manage Kong infrastructure ourselves**.
```
### Template 4: ADR for Deprecation
```markdown
# ADR-0020: Deprecate MongoDB in Favor of PostgreSQL
## Status
Accepted (Supersedes ADR-0003)
## Context
ADR-0003 (2021) chose MongoDB for user profile storage due to schema flexibility
needs. Since then:
- MongoDB's multi-document transactions remain problematic for our use case
- Our schema has stabilized and rarely changes
- We now have PostgreSQL expertise from other services
- Maintaining two databases increases operational burden
## Decision
Deprecate MongoDB and migrate user profiles to PostgreSQL.
## Migration Plan
1. **Phase 1** (Week 1-2): Create PostgreSQL schema, dual-write enabled
2. **Phase 2** (Week 3-4): Backfill historical data, validate consistency
3. **Phase 3** (Week 5): Switch reads to PostgreSQL, monitor
4. **Phase 4** (Week 6): Remove MongoDB writes, decommission
## Consequences
### Positive
- Single database technology reduces operational complexity
- ACID transactions for user data
- Team can focus PostgreSQL expertise
### Negative
- Migration effort (~4 weeks)
- Risk of data issues during migration
- Lose some schema flexibility
## Lessons Learned
Document from ADR-0003 experience:
- Schema flexibility benefits were overestimated
- Operational cost of multiple databases was underestimated
- Consider long-term maintenance in technology decisions
```
### Template 5: Request for Comments (RFC) Style
```markdown
# RFC-0025: Adopt Event Sourcing for Order Management
## Summary
Propose adopting event sourcing pattern for the order management domain to
improve auditability, enable temporal queries, and support business analytics.
## Motivation
Current challenges:
1. Audit requirements need complete order history
2. "What was the order state at time X?" queries are impossible
3. Analytics team needs event stream for real-time dashboards
4. Order state reconstruction for customer support is manual
## Detailed Design
### Event Store
```
OrderCreated { orderId, customerId, items[], timestamp }
OrderItemAdded { orderId, item, timestamp }
OrderItemRemoved { orderId, itemId, timestamp }
PaymentReceived { orderId, amount, paymentId, timestamp }
OrderShipped { orderId, trackingNumber, timestamp }
```
### Projections
- **CurrentOrderState**: Materialized view for queries
- **OrderHistory**: Complete timeline for audit
- **DailyOrderMetrics**: Analytics aggregation
### Technology
- Event Store: EventStoreDB (purpose-built, handles projections)
- Alternative considered: Kafka + custom projection service
## Drawbacks
- Learning curve for team
- Increased complexity vs. CRUD
- Need to design events carefully (immutable once stored)
- Storage growth (events never deleted)
## Alternatives
1. **Audit tables**: Simpler but doesn't enable temporal queries
2. **CDC from existing DB**: Complex, doesn't change data model
3. **Hybrid**: Event source only for order state changes
## Unresolved Questions
- [ ] Event schema versioning strategy
- [ ] Retention policy for events
- [ ] Snapshot frequency for performance
## Implementation Plan
1. Prototype with single order type (2 weeks)
2. Team training on event sourcing (1 week)
3. Full implementation and migration (4 weeks)
4. Monitoring and optimization (ongoing)
## References
- [Event Sourcing by Martin Fowler](https://martinfowler.com/eaaDev/EventSourcing.html)
- [EventStoreDB Documentation](https://www.eventstore.com/docs)
```
## ADR Management
### Directory Structure
```
docs/
├── adr/
│ ├── README.md # Index and guidelines
│ ├── template.md # Team's ADR template
│ ├── 0001-use-postgresql.md
│ ├── 0002-caching-strategy.md
│ ├── 0003-mongodb-user-profiles.md # [DEPRECATED]
│ └── 0020-deprecate-mongodb.md # Supersedes 0003
```
### ADR Index (README.md)
```markdown
# Architecture Decision Records
This directory contains Architecture Decision Records (ADRs) for [Project Name].
## Index
| ADR | Title | Status | Date |
|-----|-------|--------|------|
| 0001 | Use PostgreSQL as Primary Database | Accepted | 2024-01-10 |
| 0002 | Caching Strategy with Redis | Accepted | 2024-01-12 |
| 0003 | MongoDB for User Profiles | Deprecated | 2023-06-15 |
| 0020 | Deprecate MongoDB | Accepted | 2024-01-15 |
## Creating a New ADR
1. Copy `template.md` to `NNNN-title-with-dashes.md`
2. Fill in the template
3. Submit PR for review
4. Update this index after approval
## ADR Status
- **Proposed**: Under discussion
- **Accepted**: Decision made, implementing
- **Deprecated**: No longer relevant
- **Superseded**: Replaced by another ADR
- **Rejected**: Considered but not adopted
```
### Automation (adr-tools)
```bash
# Install adr-tools
brew install adr-tools
# Initialize ADR directory
adr init docs/adr
# Create new ADR
adr new "Use PostgreSQL as Primary Database"
# Supersede an ADR
adr new -s 3 "Deprecate MongoDB in Favor of PostgreSQL"
# Generate table of contents
adr generate toc > docs/adr/README.md
# Link related ADRs
adr link 2 "Complements" 1 "Is complemented by"
```
## Review Process
```markdown
## ADR Review Checklist
### Before Submission
- [ ] Context clearly explains the problem
- [ ] All viable options considered
- [ ] Pros/cons balanced and honest
- [ ] Consequences (positive and negative) documented
- [ ] Related ADRs linked
### During Review
- [ ] At least 2 senior engineers reviewed
- [ ] Affected teams consulted
- [ ] Security implications considered
- [ ] Cost implications documented
- [ ] Reversibility assessed
### After Acceptance
- [ ] ADR index updated
- [ ] Team notified
- [ ] Implementation tickets created
- [ ] Related documentation updated
```
## Best Practices
### Do's
- **Write ADRs early** - Before implementation starts
- **Keep them short** - 1-2 pages maximum
- **Be honest about trade-offs** - Include real cons
- **Link related decisions** - Build decision graph
- **Update status** - Deprecate when superseded
### Don'ts
- **Don't change accepted ADRs** - Write new ones to supersede
- **Don't skip context** - Future readers need background
- **Don't hide failures** - Rejected decisions are valuable
- **Don't be vague** - Specific decisions, specific consequences
- **Don't forget implementation** - ADR without action is waste
## Resources
- [Documenting Architecture Decisions (Michael Nygard)](https://cognitect.com/blog/2011/11/15/documenting-architecture-decisions)
- [MADR Template](https://adr.github.io/madr/)
- [ADR GitHub Organization](https://adr.github.io/)
- [adr-tools](https://github.com/npryce/adr-tools)
## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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