# Architecture Document Validation Checklist

**Purpose**: Validate the architecture document itself is complete, implementable, and provides clear guidance for AI agents.

**Note**: This checklist validates the ARCHITECTURE DOCUMENT only. For cross-workflow validation (PRD → Architecture → Stories alignment), use the implementation-readiness workflow.

---

## 1. Decision Completeness

### All Decisions Made

- [ ] Every critical decision category has been resolved
- [ ] All important decision categories addressed
- [ ] No placeholder text like "TBD", "[choose]", or "{TODO}" remains
- [ ] Optional decisions either resolved or explicitly deferred with rationale

### Decision Coverage

- [ ] Data persistence approach decided
- [ ] API pattern chosen
- [ ] Authentication/authorization strategy defined
- [ ] Deployment target selected
- [ ] All functional requirements have architectural support

---

## 2. Version Specificity

### Technology Versions

- [ ] Every technology choice includes a specific version number
- [ ] Version numbers are current (verified via WebSearch, not hardcoded)
- [ ] Compatible versions selected (e.g., Node.js version supports chosen packages)
- [ ] Verification dates noted for version checks

### Version Verification Process

- [ ] WebSearch used during workflow to verify current versions
- [ ] No hardcoded versions from decision catalog trusted without verification
- [ ] LTS vs. latest versions considered and documented
- [ ] Breaking changes between versions noted if relevant

---

## 3. Starter Template Integration (if applicable)

### Template Selection

- [ ] Starter template chosen (or "from scratch" decision documented)
- [ ] Project initialization command documented with exact flags
- [ ] Starter template version is current and specified
- [ ] Command search term provided for verification

### Starter-Provided Decisions

- [ ] Decisions provided by starter marked as "PROVIDED BY STARTER"
- [ ] List of what starter provides is complete
- [ ] Remaining decisions (not covered by starter) clearly identified
- [ ] No duplicate decisions that starter already makes

---

## 4. Novel Pattern Design (if applicable)

### Pattern Detection

- [ ] All unique/novel concepts from PRD identified
- [ ] Patterns that don't have standard solutions documented
- [ ] Multi-epic workflows requiring custom design captured

### Pattern Documentation Quality

- [ ] Pattern name and purpose clearly defined
- [ ] Component interactions specified
- [ ] Data flow documented (with sequence diagrams if complex)
- [ ] Implementation guide provided for agents
- [ ] Edge cases and failure modes considered
- [ ] States and transitions clearly defined

### Pattern Implementability

- [ ] Pattern is implementable by AI agents with provided guidance
- [ ] No ambiguous decisions that could be interpreted differently
- [ ] Clear boundaries between components
- [ ] Explicit integration points with standard patterns

---

## 5. Implementation Patterns

### Pattern Categories Coverage

- [ ] **Naming Patterns**: API routes, database tables, components, files
- [ ] **Structure Patterns**: Test organization, component organization, shared utilities
- [ ] **Format Patterns**: API responses, error formats, date handling
- [ ] **Communication Patterns**: Events, state updates, inter-component messaging
- [ ] **Lifecycle Patterns**: Loading states, error recovery, retry logic
- [ ] **Location Patterns**: URL structure, asset organization, config placement
- [ ] **Consistency Patterns**: UI date formats, logging, user-facing errors

### Pattern Quality

- [ ] Each pattern has concrete examples
- [ ] Conventions are unambiguous (agents can't interpret differently)
- [ ] Patterns cover all technologies in the stack
- [ ] No gaps where agents would have to guess
- [ ] Implementation patterns don't conflict with each other

---

## 6. Technology Compatibility

### Stack Coherence

- [ ] Database choice compatible with ORM choice
- [ ] Frontend framework compatible with deployment target
- [ ] Authentication solution works with chosen frontend/backend
- [ ] All API patterns consistent (not mixing REST and GraphQL for same data)
- [ ] Starter template compatible with additional choices

### Integration Compatibility

- [ ] Third-party services compatible with chosen stack
- [ ] Real-time solutions (if any) work with deployment target
- [ ] File storage solution integrates with framework
- [ ] Background job system compatible with infrastructure

---

## 7. Document Structure

### Required Sections Present

- [ ] Executive summary exists (2-3 sentences maximum)
- [ ] Project initialization section (if using starter template)
- [ ] Decision summary table with ALL required columns:
  - Category
  - Decision
  - Version
  - Rationale
- [ ] Project structure section shows complete source tree
- [ ] Implementation patterns section comprehensive
- [ ] Novel patterns section (if applicable)

### Document Quality

- [ ] Source tree reflects actual technology decisions (not generic)
- [ ] Technical language used consistently
- [ ] Tables used instead of prose where appropriate
- [ ] No unnecessary explanations or justifications
- [ ] Focused on WHAT and HOW, not WHY (rationale is brief)

---

## 8. AI Agent Clarity

### Clear Guidance for Agents

- [ ] No ambiguous decisions that agents could interpret differently
- [ ] Clear boundaries between components/modules
- [ ] Explicit file organization patterns
- [ ] Defined patterns for common operations (CRUD, auth checks, etc.)
- [ ] Novel patterns have clear implementation guidance
- [ ] Document provides clear constraints for agents
- [ ] No conflicting guidance present

### Implementation Readiness

- [ ] Sufficient detail for agents to implement without guessing
- [ ] File paths and naming conventions explicit
- [ ] Integration points clearly defined
- [ ] Error handling patterns specified
- [ ] Testing patterns documented

---

## 9. Practical Considerations

### Technology Viability

- [ ] Chosen stack has good documentation and community support
- [ ] Development environment can be set up with specified versions
- [ ] No experimental or alpha technologies for critical path
- [ ] Deployment target supports all chosen technologies
- [ ] Starter template (if used) is stable and well-maintained

### Scalability

- [ ] Architecture can handle expected user load
- [ ] Data model supports expected growth
- [ ] Caching strategy defined if performance is critical
- [ ] Background job processing defined if async work needed
- [ ] Novel patterns scalable for production use

---

## 10. Common Issues to Check

### Beginner Protection

- [ ] Not overengineered for actual requirements
- [ ] Standard patterns used where possible (starter templates leveraged)
- [ ] Complex technologies justified by specific needs
- [ ] Maintenance complexity appropriate for team size

### Expert Validation

- [ ] No obvious anti-patterns present
- [ ] Performance bottlenecks addressed
- [ ] Security best practices followed
- [ ] Future migration paths not blocked
- [ ] Novel patterns follow architectural principles

---

## Validation Summary

### Document Quality Score

- Architecture Completeness: [Complete / Mostly Complete / Partial / Incomplete]
- Version Specificity: [All Verified / Most Verified / Some Missing / Many Missing]
- Pattern Clarity: [Crystal Clear / Clear / Somewhat Ambiguous / Ambiguous]
- AI Agent Readiness: [Ready / Mostly Ready / Needs Work / Not Ready]

### Critical Issues Found

{ list issues }

### Recommended Actions Before Implementation

{ list actions }

---

**Next Step**: Run the **implementation-readiness** workflow to validate alignment between PRD, Architecture, and Stories before beginning implementation.

---

_This checklist validates architecture document quality only. Use implementation-readiness for comprehensive readiness validation._
