decision-records

id	SKL-decision-DECISIONRECORDS
name	Decision Records
description	Architecture Decision Records (ADRs) are lightweight documents that capture important architectural decisions and their rationale, providing a historical record that helps teams understand the "why" b
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

Decision Records

Skill Profile

(Select at least one profile to enable specific modules)

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

Overview

Architecture Decision Records (ADRs) are lightweight documents that capture important architectural decisions and their rationale, providing a historical record that helps teams understand the "why" behind past choices. This skill provides a systematic approach to creating, managing, and maintaining ADRs, reducing time spent re-debating settled decisions and preventing knowledge loss when team members change. ADRs serve as both a communication tool for current team members and a learning resource for understanding system evolution over time.

Why This Matters

• Preserves Knowledge: Decisions outlive team members; ADRs prevent knowledge loss during turnover
• Reduces Re-debates: Clear documentation stops teams from revisiting settled decisions repeatedly
• Improves Decision Quality: Writing decisions forces thorough thinking about alternatives and trade-offs
• Enables Onboarding: New team members quickly understand architectural choices without needing to hunt through history
• Provides Accountability: Clear ownership and documentation of who made decisions and why

---

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:
  • Decision context and requirements
  • Alternative options being considered
  • Constraints and trade-offs
  • Stakeholder feedback
• Entry Conditions:
  • Decision meets significance threshold
  • Key stakeholders are available for review
  • Sufficient information available to make decision
• Outputs:
  • ADR document with standard template
  • Decision status (Proposed/Accepted/Deprecated/Superseded)
  • Documented consequences (positive and negative)
  • Alternatives considered with rationale
• Artifacts Required (Deliverables):
  • ADR markdown file
  • Links to related ADRs
  • Implementation notes (if applicable)
• Acceptance Evidence:
  • ADR reviewed and approved by stakeholders
  • Status marked as "Accepted"
  • ADR stored in version control
  • Related ADRs linked
• Success Criteria:
  • Decision rationale clearly documented
  • Alternatives thoroughly considered
  • Consequences honestly listed (positive and negative)
  • ADR discoverable and accessible to team

Skill Composition

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

---

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