Media Library

Media Library

Skill Profile

(Select at least one profile to enable specific modules)

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

Overview

A media library manages digital assets including images, videos, and documents. This guide covers upload, processing, organization, and CDN integration for building scalable media management systems.

Why This Matters

• Performance: Optimized media reduces bandwidth and improves load times
• Scalability: Proper storage and CDN integration handles growing media libraries
• User Experience: Fast, reliable media delivery improves user satisfaction
• Security: Proper validation prevents malicious file uploads

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:
  • File uploads (images, videos, documents)
  • Folder structure for organization
  • Metadata extraction requirements
  • CDN configuration settings
• Entry Conditions:
  • Cloud storage account configured (S3, GCS, etc.)
  • CDN distribution set up (CloudFront, Cloudflare, etc.)
  • Database schema created for media metadata
  • File processing libraries installed (Sharp, FFmpeg)
• Outputs:
  • Processed media files with optimized formats
  • Generated thumbnails and derivatives
  • CDN URLs for media delivery
  • Metadata records in database
• Artifacts Required (Deliverables):
  • Media upload API endpoints
  • File processing pipeline
  • Database schema and migrations
  • CDN integration configuration
• Acceptance Evidence:
  • Successful upload test report
  • Processing performance benchmarks
  • CDN delivery speed metrics
  • Security audit results
• Success Criteria:
  • Upload success rate ≥ 99%
  • Image processing time < 5 seconds
  • CDN cache hit rate ≥ 80%
  • File size reduction ≥ 30% after optimization

Skill Composition

• Depends on: File Storage (13-file-storage/), Authentication (10-authentication-authorization/)
• Compatible with: Contentful Integration (33-content-management/contentful-integration/), CDN Delivery (37-video-streaming/cdn-delivery/)
• Conflicts with: None
• Related Skills: contentful-integration, file-storage

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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