architectural-reviews

id	SKL-architectural-ARCHITECTURALREVIEWS
name	Architectural Reviews
description	Architectural Reviews is a critical process for evaluating system designs before implementation, helping to reduce risks in large-scale systems where incorrect early decisions can cause millions of do
version	1.0.0
status	active
owner	@cerebra-team
last_updated	2026-02-22
category	Backend
tags	["api","backend","server","database"]
stack	["Python","Node.js","REST API","GraphQL"]
difficulty	Intermediate

Architectural Reviews

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

Architectural Reviews is a critical process for evaluating system designs before implementation, helping to reduce risks in large-scale systems where incorrect early decisions can cause millions of dollars in damage and months of repair time. This skill provides a comprehensive framework for conducting systematic reviews that assess requirements, scalability, security, maintainability, and operational considerations. It enables teams to make informed architectural decisions that support long-term system health and business objectives.

Why This Matters

• Reduces Technical Debt: Effective architectural reviews prevent costly rework and debt accumulation over the system lifecycle
• Increases System Stability: Identifies potential design flaws before production, reducing downtime and operational issues
• Improves Team Velocity: Provides clear design guidance that helps development teams work more efficiently
• Reduces Maintenance Costs: Proactively addresses issues that would otherwise require expensive fixes later
• Ensures Investment Confidence: Gives executives and stakeholders confidence that technical investments are sound

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Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • Architectural documents (C4 diagrams, sequence diagrams)
  • Requirements documentation (functional and non-functional)
  • Architecture Decision Records (ADRs)
  • Technology stack proposal
  • Cost analysis and resource estimates
• Entry Conditions:
  • Project requirements are documented
  • Initial architectural design is prepared
  • Review team is assembled and available
  • Materials are shared 48 hours in advance
• Outputs:
  • Review report with status (approved/rejected/deferred)
  • Documented decisions with rationale
  • Action items with owners and due dates
  • Updated architecture diagrams
  • ADRs for key decisions
• Artifacts Required (Deliverables):
  • Review report (markdown or PDF)
  • Updated architecture diagrams
  • ADRs for approved decisions
  • Action item tracking document
• Acceptance Evidence:
  • Signed/approved review report
  • Completed action items
  • Updated documentation in repository
• Success Criteria:
  • All critical concerns addressed
  • Action items tracked to completion
  • Decisions documented with clear rationale
  • Stakeholder alignment achieved

Skill Composition

• Depends on: decision-records, system-thinking
• Compatible with: risk-assessment, technical-debt-management
• Conflicts with: None
• Related Skills: code-review, problem-framing

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Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Type	Focus Area	Required Scenarios / Mocks
Unit	Core Logic	Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage
Integration	DB / API	All external API calls or database connections must be mocked during unit tests
E2E	User Journey	Critical user flows to test
Performance	Latency / Load	Benchmark requirements
Security	Vuln / Auth	SAST/DAST or dependency audit
Frontend	UX / A11y	Accessibility checklist (WCAG), Performance Budget (Lighthouse score)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure