Architecture Decision Records V3

Architecture Decision Records

Overview

This public intake copy packages plugins/antigravity-bundle-architecture-design/skills/architecture-decision-records from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Architecture Decision Records Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, Templates, Status, Context, Decision Drivers, Considered Options.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Making significant architectural decisions
• Documenting technology choices
• Recording design trade-offs
• Onboarding new team members
• Reviewing historical decisions
• Establishing decision-making processes

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Capture the decision context, constraints, and drivers.
2. Document considered options with tradeoffs.
3. Record the decision, rationale, and consequences.
4. Link related ADRs and update status over time.
5. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
6. Read the overview and provenance files before loading any copied upstream support files.
7. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.

Imported Workflow Notes

Imported: Instructions

1. Capture the decision context, constraints, and drivers.
2. Document considered options with tradeoffs.
3. Record the decision, rationale, and consequences.
4. Link related ADRs and update status over time.

Imported: Review Process

#### Imported: Summary

Propose adopting event sourcing pattern for the order management domain to
improve auditability, enable temporal queries, and support business analytics.

#### Imported: 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

## Examples

### Example 1: Ask for the upstream workflow directly

```text
Use @architecture-decision-records-v3 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @architecture-decision-records-v3 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @architecture-decision-records-v3 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @architecture-decision-records-v3 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• 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't change accepted ADRs - Write new ones to supersede
• Don't skip context - Future readers need background

Imported Operating Notes

Imported: 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

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-bundle-architecture-design/skills/architecture-decision-records, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @agent-evaluation-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-agents-architect-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @airtable-automation-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @architecture-patterns-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: References

• PostgreSQL JSON Documentation
• PostgreSQL Full Text Search
• 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

#### Imported: References

- [Event Sourcing by Martin Fowler](https://martinfowler.com/eaaDev/EventSourcing.html)
- [EventStoreDB Documentation](https://www.eventstore.com/docs)

Imported: Index

Imported: Resources

• Documenting Architecture Decisions (Michael Nygard)
• MADR Template
• ADR GitHub Organization
• adr-tools

Imported: Templates

Template 1: Standard ADR (MADR Format)

# ADR-0001: Use PostgreSQL as Primary Database

#### Imported: Status

Accepted

#### Imported: 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.

#### Imported: 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

#### Imported: 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

#### Imported: Decision

We will use **PostgreSQL 15** as our primary database.

#### Imported: 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).

#### Imported: 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

#### Imported: 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

#### Imported: 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.

#### Imported: Decision

Adopt TypeScript for all new frontend code. Migrate existing code incrementally.

#### Imported: 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

# 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

# ADR-0020: Deprecate MongoDB in Favor of PostgreSQL

#### Imported: Status

Accepted (Supersedes ADR-0003)

#### Imported: 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

#### Imported: Decision

Deprecate MongoDB and migrate user profiles to PostgreSQL.

#### Imported: 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

#### Imported: 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

#### Imported: 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

# RFC-0025: Adopt Event Sourcing for Order Management

#### Imported: 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

#### Imported: 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

#### Imported: Drawbacks

- Learning curve for team
- Increased complexity vs. CRUD
- Need to design events carefully (immutable once stored)
- Storage growth (events never deleted)

#### Imported: 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

#### Imported: Unresolved Questions

- [ ] Event schema versioning strategy
- [ ] Retention policy for events
- [ ] Snapshot frequency for performance

#### Imported: 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)

#### Imported: 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].

#### Imported: 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

#### Imported: 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)

# 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"

Imported: 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

#### Imported: 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.