Env Matrix Dev Stg Prod

Env Matrix Dev Stg Prod

Skill Profile

(Select at least one profile to enable specific modules)

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

Overview

Standards for managing environments (development, staging, production) including configs, data policies, and access controls that differ per environment. This skill enables environment parity, reduces "works on my machine" issues, and ensures proper safety controls and compliance.

Why This Matters

• Parity: Environments are as similar as possible, reducing "works on my machine"
• Safety: Production protected, staging for testing
• Clarity: Know what each environment can do
• Compliance: Proper access controls

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:
  • Environment variable definitions (APP_ENV, environment-specific configs)
  • Access control policies and role definitions
  • Data sanitization/masking rules
  • Secret manager configuration per environment
• Entry Conditions:
  • Secret manager is configured for each environment
  • Access control policies are defined
  • Data sanitization pipeline is set up (for staging)
• Outputs:
  • Environment-specific configuration files
  • Access control matrix documentation
  • Data policy documentation
  • Deployment flow documentation
• Artifacts Required (Deliverables):
  • Environment configuration matrix
  • Access control matrix
  • Data policy document
  • Secret manager setup scripts
• Acceptance Evidence:
  • Staging deployment flow matches production
  • Secrets are separated per environment
  • Access controls are enforced
  • Data policies are documented and implemented
• Success Criteria:
  • Staging environment is production-like (same topology, config shape)
  • Zero secret reuse across environments
  • All production changes go through staging first

Skill Composition

• Depends on: config-distribution
• Compatible with: deployment-patterns, observability-packaging
• Conflicts with: Sharing secrets across environments, using production data in dev/staging
• Related Skills: secrets-key-management, tenant-aware-ops

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)

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

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