| name | system-architecture-docs |
| description | System architecture documentation describes structure, behavior, and Use when this capability is needed. |
| metadata | {"author":"amnadtaowsoam"} |
System Architecture Docs
Skill Profile
(Select at least one profile to enable specific modules)
Overview
System architecture documentation describes structure, behavior, and design of software systems. Effective architecture documentation uses diagrams, decision records, and clear explanations to help teams understand system design, make informed decisions, and onboard new members.
Why This Matters
- Knowledge Sharing: Onboards new developers, reduces knowledge silos, preserves institutional knowledge
- Decision Making: Provides context for decisions, documents trade-offs, supports future changes
- Maintenance: Guides system evolution, supports debugging, facilitates refactoring
- Communication: Aligns stakeholders, bridges technical gaps, supports discussions
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:
- System requirements and specifications
- Architecture decisions and trade-offs
- Component interfaces and APIs
- Data models and schemas
- Entry Conditions:
- System is designed and components identified
- Key decisions have been made
- Technology stack is determined
- Outputs:
- Complete architecture documentation
- C4 diagrams (Context, Container, Component, Code)
- Architecture Decision Records (ADRs)
- Data flow and sequence diagrams
- Artifacts Required (Deliverables):
- Architecture document in Markdown format
- Diagrams (Mermaid, PlantUML, or Draw.io)
- ADRs for major decisions
- Acceptance Evidence:
- Diagrams accurately represent system
- ADRs document all major decisions
- Documentation is accessible and searchable
- Success Criteria:
-
80% of services have architecture docs
- Diagram accuracy >95%
- ADR completeness >90%
- Search accuracy >90%
Skill Composition
Quick Start / Implementation Example
- Review requirements and constraints
- Set up development environment
- Implement core functionality following patterns
- Write tests for critical paths
- Run tests and fix issues
- Document any deviations or decisions
def example_function():
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
2. Performance & Resources
3. Architecture & Scalability
4. Observability & Reliability
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
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
Converted and distributed by TomeVault — claim your Tome and manage your conversions.