Alphago Architecture

Skill Profile

(Select at least one profile to enable specific modules)

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

Overview

AlphaGo Architecture is the God-Mode protocol that applies Monte Carlo Tree Search (MCTS) principles to software engineering architecture design. Instead of relying on intuition or best practices from documentation, this protocol creates thousands of simulated architecture alternatives, subjects them to synthetic workload testing, and mathematically selects the optimal solution. The result is architecture that is "proven best" through quantitative analysis rather than subjective opinion, delivering systems that are faster, more resilient, and more scalable than human-designed alternatives.

Why This Matters

• Mathematical Proof: Architecture decisions are backed by quantitative evidence, not opinion
• Optimal Performance: Discovers designs that humans would never consider
• Risk Mitigation: Identifies failure modes before deployment
• Cost Optimization: Finds the most efficient architecture for given requirements
• Future-Proof: Designs are tested against synthetic workloads representing future scenarios

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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:
  • System requirements and constraints
  • Performance targets and SLAs
  • Resource limits and budget constraints
  • Technology stack preferences and restrictions
  • Historical workload data or traffic patterns
• Entry Conditions:
  • LLM with architecture design capabilities is available
  • Simulation framework is configured
  • Synthetic workload generator is available
  • Performance monitoring and metrics collection is set up
• Outputs:
  • Optimal architecture design with mathematical justification
  • Performance comparison of top alternatives
  • Architecture Decision Records (ADRs)
  • Implementation blueprint with detailed specifications
  • Risk assessment and mitigation strategies
• Artifacts Required (Deliverables):
  • Architecture diagrams and specifications
  • Simulation results and performance metrics
  • Comparison matrix of design alternatives
  • ADRs documenting key decisions
  • Implementation roadmap
• Acceptance Evidence:
  • Selected architecture meets all performance targets
  • Simulation results are statistically significant
  • Architecture is implementable within constraints
  • Risk assessment is comprehensive
  • Documentation is complete and actionable
• Success Criteria:
  • Simulation coverage: ≥10,000 architecture variants
  • Performance prediction accuracy: ≥90%
  • Architecture meets all SLAs in simulation
  • Implementation complexity: within acceptable range
  • Cost efficiency: ≥20% improvement over baseline

Skill Composition

• Depends on: `cognitive-governance`, `algorithmic-self-discovery`
• Compatible with: `system-design`, `performance-engineering`
• Conflicts with: `quick-prototyping` (simulation vs speed)
• Related Skills: `database-design`, `microservices-architecture`

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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