MoAI Foundation Core

Foundational principles and architectural patterns that power MoAI-ADK's AI-driven development workflow.

Core Philosophy: Quality-first, test-driven, modular, and efficient AI development through proven patterns and automated workflows.

Quick Reference (30 seconds)

What is MoAI Foundation Core? Six essential principles that ensure quality, efficiency, and scalability in AI-powered development:

1. TRUST 5 Framework - Quality gate system (Test-first, Readable, Unified, Secured, Trackable)
2. SPEC-First TDD - Specification-driven test-driven development workflow
3. Delegation Patterns - Task orchestration via specialized agents (never direct execution)
4. Token Optimization - 200K budget management and context efficiency
5. Progressive Disclosure - Three-tier knowledge delivery (Quick → Implementation → Advanced)
6. Modular System - File splitting and reference architecture for scalability

Quick Access:

• Quality standards → TRUST 5 Module
• Development workflow → SPEC-First TDD Module
• Agent coordination → Delegation Patterns Module
• Budget management → Token Optimization Module
• Content structure → Progressive Disclosure Module
• File organization → Modular System Module
• Agent catalog → Agents Reference Module NEW
• Command reference → Commands Reference Module NEW
• Security & constraints → Execution Rules Module NEW

Use Cases:

• New agent creation with quality standards
• New skill development with structural guidelines
• Complex workflow orchestration
• Token budget planning and optimization
• Documentation architecture design
• Quality gate configuration

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Implementation Guide (5 minutes)

1. TRUST 5 Framework - Quality Assurance System

Purpose: Automated quality gates ensuring code quality, security, and maintainability.

Five Pillars:

Test-first Pillar:

• Requirement: Maintain test coverage at or above 85 percent
• Validation: Execute pytest with coverage reporting
• Failure Action: Block merge and generate missing tests
• WHY: High coverage ensures code reliability and reduces production defects
• IMPACT: Catches bugs early, reduces debugging time by 60-70 percent

Readable Pillar:

• Requirement: Use clear and descriptive naming conventions
• Validation: Execute ruff linter checks
• Failure Action: Issue warning and suggest refactoring improvements
• WHY: Clear naming improves code comprehension and team collaboration
• IMPACT: Reduces onboarding time by 40 percent, improves maintenance velocity

Unified Pillar:

• Requirement: Apply consistent formatting and import patterns
• Validation: Execute black formatter and isort checks
• Failure Action: Auto-format code or issue warning
• WHY: Consistency eliminates style debates and merge conflicts
• IMPACT: Reduces code review time by 30 percent, improves readability

Secured Pillar:

• Requirement: Comply with OWASP security standards
• Validation: Execute security-expert agent analysis
• Failure Action: Block merge and require security review
• WHY: Security vulnerabilities create critical business and legal risks
• IMPACT: Prevents 95+ percent of common security vulnerabilities

Trackable Pillar:

• Requirement: Write clear and structured commit messages
• Validation: Match Git commit message regex patterns
• Failure Action: Suggest proper commit message format
• WHY: Clear history enables debugging, auditing, and collaboration
• IMPACT: Reduces issue investigation time by 50 percent

Integration Points:

• Pre-commit hooks → Automated validation
• CI/CD pipelines → Quality gate enforcement
• Agent workflows → core-quality validation
• Documentation → Quality metrics

Detailed Reference: TRUST 5 Framework Module

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2. SPEC-First TDD - Development Workflow

Purpose: Specification-driven development ensuring clear requirements before implementation.

Three-Phase Workflow:

Phase 1: SPEC (/moai:1-plan)
 workflow-spec → EARS format
 Output: .moai/specs/SPEC-XXX/spec.md
 Execute /clear (saves 45-50K tokens)

Phase 2: TDD (/moai:2-run)
 RED: Failing tests
 GREEN: Passing code
 REFACTOR: Optimize
 Validate: ≥85% coverage

Phase 3: Docs (/moai:3-sync)
 API documentation
 Architecture diagrams
 Project reports

EARS Format:

• Ubiquitous: System-wide (always active)
• Event-driven: Trigger-based (when X, do Y)
• State-driven: Conditional (while X, do Y)
• Unwanted: Prohibited (shall not do X)
• Optional: Nice-to-have (where possible, do X)

Token Budget: SPEC 30K | TDD 180K | Docs 40K | Total 250K

Key Practice: Execute /clear after Phase 1 to initialize context.

Detailed Reference: SPEC-First TDD Module

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3. Delegation Patterns - Agent Orchestration

Purpose: Task delegation to specialized agents, avoiding direct execution.

Core Principle [HARD]: Alfred must delegate all work through Task() to specialized agents.

WHY: Direct execution bypasses specialization, quality gates, and token optimization. IMPACT: Proper delegation improves task success rate by 40 percent and enables parallel execution.

Delegation Syntax:

result = await Task(
 subagent_type="specialized_agent",
 prompt="Clear, specific task",
 context={"relevant": "data"}
)

Three Patterns:

Sequential (dependencies):

design = Task(subagent_type="api-designer", prompt="Design API")
code = Task(subagent_type="backend-expert", prompt="Implement", context={"design": design})

Parallel (independent):

results = await Promise.all([
 Task(subagent_type="backend-expert", prompt="Backend"),
 Task(subagent_type="frontend-expert", prompt="Frontend")
])

Conditional (analysis-based):

analysis = Task(subagent_type="debug-helper", prompt="Analyze")
if analysis.type == "security":
 Task(subagent_type="security-expert", prompt="Fix")

Agent Selection:

• Simple (1 file): 1-2 agents sequential
• Medium (3-5 files): 2-3 agents sequential
• Complex (10+ files): 5+ agents mixed

Detailed Reference: Delegation Patterns Module

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4. Token Optimization - Budget Management

Purpose: Efficient 200K token budget through strategic context management.

Budget Allocation:

SPEC Phase:

• Token Budget: 30K tokens
• Strategy: Load requirements only, execute /clear after completion
• WHY: Specification phase requires minimal context for requirement analysis
• IMPACT: Saves 45-50K tokens for implementation phase

TDD Phase:

• Token Budget: 180K tokens
• Strategy: Selective file loading, load only implementation-relevant files
• WHY: Implementation requires deep context but not full codebase
• IMPACT: Enables 70 percent larger implementations within budget

Docs Phase:

• Token Budget: 40K tokens
• Strategy: Result caching and template reuse
• WHY: Documentation builds on completed work artifacts
• IMPACT: Reduces redundant file reads by 60 percent

Total Budget:

• Combined Budget: 250K tokens across all phases
• Strategy: Phase separation with context reset between phases
• WHY: Clean context boundaries prevent token bloat
• IMPACT: Enables 2-3x larger projects within same budget

Token Saving Strategies:

1. Phase Separation: /clear between phases

• After /moai:1-plan (saves 45-50K)
• When context > 150K
• After 50+ messages

2. Selective Loading: Load only necessary files

3. Context Optimization: 20-30K tokens target

4. Model Selection: Sonnet (quality) | Haiku (speed/cost)

Monitoring: /context command, track budget, suggest /clear

Cost Savings: Haiku 70% cheaper → 60-70% total savings

Detailed Reference: Token Optimization Module

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5. Progressive Disclosure - Content Architecture

Purpose: Three-tier knowledge delivery balancing value with depth.

Three Levels:

Quick Reference Level:

• Time Investment: 30 seconds
• Content: Core principles and essential concepts
• Token Usage: Approximately 1,000 tokens
• WHY: Rapid value delivery for time-constrained users
• IMPACT: Users gain 80 percent understanding in 5 percent of time

Implementation Level:

• Time Investment: 5 minutes
• Content: Workflows, practical examples, integration patterns
• Token Usage: Approximately 3,000 tokens
• WHY: Bridges concept to execution with actionable guidance
• IMPACT: Enables immediate productive work without deep expertise

Advanced Level:

• Time Investment: 10+ minutes
• Content: Deep technical dives, edge cases, optimization techniques
• Token Usage: Approximately 5,000 tokens
• WHY: Provides mastery-level knowledge for complex scenarios
• IMPACT: Reduces escalations by 70 percent through comprehensive coverage

SKILL.md Structure (≤500 lines):

## Quick Reference (30s)
## Implementation Guide (5min)
## Advanced Patterns (10+min)
## Works Well With

Module Architecture:

• SKILL.md: Entry point, cross-references
• modules/: Deep dives, unlimited
• examples.md: Working samples
• reference.md: External links

File Splitting (when >500 lines):

SKILL.md (500 lines)
 Quick (80-120)
 Implementation (180-250)
 Advanced (80-140)
 References (10-20)

Overflow → modules/[topic].md

Detailed Reference: Progressive Disclosure Module

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6. Modular System - File Organization

Purpose: Scalable file structure enabling unlimited content.

Standard Structure:

.claude/skills/skill-name/
 SKILL.md # Core (≤500 lines)
 modules/ # Extended (unlimited)
 patterns.md
 examples.md # Working samples
 reference.md # External links
 scripts/ # Utilities (optional)
 templates/ # Templates (optional)

File Principles:

1. SKILL.md: ≤500 lines, progressive disclosure, cross-references
2. modules/: Topic-focused, no limits, self-contained
3. examples.md: Copy-paste ready, commented
4. reference.md: API docs, resources

Cross-Reference Syntax:

Details: [Module](modules/patterns.md)
Examples: [Examples](examples.md#auth)
External: [Reference](reference.md#api)

Discovery Flow: SKILL.md → Topic → modules/[topic].md → Deep dive

Detailed Reference: Modular System Module

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Advanced Implementation (10+ minutes)

Advanced patterns including cross-module integration, quality validation, and error handling are available in the detailed module references.

Key Advanced Topics:

• Cross-Module Integration: Combining TRUST 5 + SPEC-First TDD
• Token-Optimized Delegation: Parallel execution with context reset
• Progressive Agent Workflows: Escalation patterns
• Quality Validation: Pre/Post execution validation
• Error Handling: Delegation failure recovery

Detailed Reference: examples.md for working code samples

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Works Well With

Agents:

• agent-factory - Create agents with foundation principles
• skill-factory - Generate skills with modular architecture
• core-quality - Automated TRUST 5 validation
• workflow-spec - EARS format specification
• workflow-tdd - RED-GREEN-REFACTOR execution
• workflow-docs - Documentation with progressive disclosure

Skills:

• moai-cc-claude-md - CLAUDE.md with foundation patterns
• moai-cc-configuration - Config with TRUST 5
• moai-cc-memory - Token optimization
• moai-context7-integration - MCP integration

Tools:

• AskUserQuestion - Use directly for all user interaction and clarification needs

Commands:

• /moai:1-plan - SPEC-First Phase 1
• /moai:2-run - TDD Phase 2
• /moai:3-sync - Documentation Phase 3
• /moai:9-feedback - Continuous improvement
• /clear - Token management

Foundation Modules (Extended Documentation):

• Agents Reference - 26-agent catalog with 7-tier hierarchy
• Commands Reference - 6 core commands workflow
• Execution Rules - Security, Git strategy, compliance

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Quick Decision Matrix

Scenario	Primary Principle	Supporting Principles
New Agent	TRUST 5, Delegation	Token Opt, Modular
New Skill	Progressive, Modular	TRUST 5, Token Opt
Workflow	Delegation Patterns	SPEC-First, Token Opt
Quality	TRUST 5 Framework	SPEC-First TDD
Budget	Token Optimization	Progressive, Modular
Docs	Progressive, Modular	Token Optimization

Module Deep Dives:

• TRUST 5 Framework
• SPEC-First TDD
• Delegation Patterns
• Token Optimization
• Progressive Disclosure
• Modular System
• Agents Reference NEW
• Commands Reference NEW
• Execution Rules NEW

Full Examples: examples.md External Resources: reference.md

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Version: 2.4.0 Last Updated: 2025-12-06 Status: Active (optimized to <500 lines)