Env Matrix Dev Stg Prod

Skill Profile

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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

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

# 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