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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
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SOC 職業分類に基づく
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| 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 |
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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.
# Example implementation following best practices
def example_function():
# Your implementation here
pass
.env.example keys: API_KEY, DATABASE_URL (no values)| 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) |
request_iderror_rate, latency, queue_depth(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)