python-standards

id	SKL-python-PYTHONSTANDARDS
name	Python Standards
description	Python coding standards define best practices for writing clean, maintainable, and robust Python code. This skill covers PEP 8 compliance, modern type hints with Python 3.10+ syntax, Pydantic models f
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

Python Standards

Skill Profile

(Select at least one profile to enable specific modules)

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

Overview

Python coding standards define best practices for writing clean, maintainable, and robust Python code. This skill covers PEP 8 compliance, modern type hints with Python 3.10+ syntax, Pydantic models for validation, async/await patterns, structured logging, comprehensive error handling, dependency injection patterns, and testing strategies. Following these standards improves code quality, reduces bugs, enhances IDE support, and enables effective collaboration across Python projects.

Why This Matters

• Improves Code Quality: Type hints and validation catch errors early; PEP 8 compliance ensures consistent, readable code
• Reduces Bugs: Comprehensive error handling and proper validation prevent runtime exceptions and data corruption
• Enhances Maintainability: Clean code patterns and proper abstractions make code easier to understand, modify, and extend
• Enables Better Tooling: Type hints improve IDE autocomplete and error detection; linters catch issues automatically
• Supports AI/ML Development: Modern Python patterns (async, type hints, dataclasses) are essential for AI/ML workflows
• Facilitates Team Collaboration: Consistent coding standards make code reviews more effective and onboarding smoother for new team members

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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:
  • Python code files
  • Requirements and dependencies
  • API specifications
  • Business requirements
• Entry Conditions:
  • Python 3.11+ environment is configured
  • Required packages are installed
  • Project structure is established
• Outputs:
  • Clean, PEP 8 compliant code
  • Type-annotated code
  • Validated data models
  • Comprehensive test coverage
  • Structured logging output
• Artifacts Required (Deliverables):
  • Python source code
  • Type stubs (.pyi files if needed)
  • Test files with >80% coverage
  • Pydantic models
  • Logging configuration
• Acceptance Evidence:
  • Code passes linting (Black, Ruff, Mypy)
  • Type checking passes (Mypy strict mode)
  • All tests pass (pytest)
  • Test coverage >80%
  • Code follows PEP 8 standards
• Success Criteria:
  • Code is clean and maintainable
  • Type hints are comprehensive
  • Tests provide good coverage
  • Logging is structured and contextual
  • Error handling is comprehensive

Skill Composition

• Depends on: code-review, api-design
• Compatible with: refactoring-strategies, error-handling
• Conflicts with: None
• Related Skills: fastapi-patterns, validation

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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