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MoAI-ADK's foundational principles - TRUST 5, SPEC-First TDD, delegation patterns, token optimization, progressive disclosure, modular architecture, agent catalog, command reference, and execution rules for building AI-powered development workflows
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25 archivos| name | moai-foundation-core |
| description | MoAI-ADK's foundational principles - TRUST 5, SPEC-First TDD, delegation patterns, token optimization, progressive disclosure, modular architecture, agent catalog, command reference, and execution rules for building AI-powered development workflows |
| version | 2.2.0 |
| modularized | true |
| updated | "2025-11-26T00:00:00.000Z" |
| status | active |
| tags | ["foundation","core","orchestration","agents","commands","trust-5","spec-first-tdd"] |
| allowed-tools | Read, Grep, Glob |
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:
- TRUST 5 Framework - Quality gate system (Test-first, Readable, Unified, Secured, Trackable)
- SPEC-First TDD - Specification-driven test-driven development workflow
- Delegation Patterns - Task orchestration via specialized agents (never direct execution)
- Token Optimization - 200K budget management and context efficiency
- Progressive Disclosure - Three-tier knowledge delivery (Quick → Implementation → Advanced)
- Modular System - File splitting and reference architecture for scalability
- 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
Implementation Guide (5 minutes)
1. TRUST 5 Framework - Quality Assurance System
Purpose: Automated quality gates ensuring code quality, security, and maintainability.
| Pillar | Requirement | Validation | Failure Action |
|---|
| Test-first | ≥85% coverage | pytest --cov | Block merge, generate tests |
| Readable | Clear naming | ruff linter | Warning, suggest refactor |
| Unified | Consistent patterns | black, isort | Auto-format or warn |
| Secured | OWASP compliance | security-expert | Block merge, review |
| Trackable | Clear commits | Git regex | Suggest format |
- Pre-commit hooks → Automated validation
- CI/CD pipelines → Quality gate enforcement
- Agent workflows → core-quality validation
- Documentation → Quality metrics
2. SPEC-First TDD - Development Workflow
Purpose: Specification-driven development ensuring clear requirements before implementation.
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
- 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.
3. Delegation Patterns - Agent Orchestration
Purpose: Task delegation to specialized agents, avoiding direct execution.
Core Principle: Alfred NEVER executes directly. All work via Task().
result = await Task(
subagent_type="specialized_agent",
prompt="Clear, specific task",
context={"relevant": "data"}
)
Sequential (dependencies):
design = Task(subagent_type="api-designer", prompt="Design API")
code = Task(subagent_type="backend-expert", prompt="Implement", context={"design": design})
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")
- Simple (1 file): 1-2 agents sequential
- Medium (3-5 files): 2-3 agents sequential
- Complex (10+ files): 5+ agents mixed
4. Token Optimization - Budget Management
Purpose: Efficient 200K token budget through strategic context management.
| Phase | Budget | Strategy |
|---|
| SPEC | 30K | Load requirements only, /clear after |
| TDD | 180K | Selective file loading |
| Docs | 40K | Result caching, templates |
| Total | 250K | Phase separation |
- Phase Separation: /clear between phases
- After /moai:1-plan (saves 45-50K)
- When context > 150K
- After 50+ messages
-
Selective Loading: Load only necessary files
-
Context Optimization: 20-30K tokens target
-
Model Selection: Sonnet (quality) | Haiku (speed/cost)
Monitoring: /context command, track budget, suggest /clear
Cost Savings: Haiku 70% cheaper → 60-70% total savings
5. Progressive Disclosure - Content Architecture
Purpose: Three-tier knowledge delivery balancing value with depth.
| Level | Time | Content | Tokens |
|---|
| Quick Reference | 30s | Core principles | 1,000 |
| Implementation | 5min | Workflows, examples | 3,000 |
| Advanced | 10+min | Deep dives, edge cases | 5,000 |
SKILL.md Structure (≤500 lines):
## Quick Reference (30s)
## Implementation Guide (5min)
## Advanced Patterns (10+min)
## Works Well With
- 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
6. Modular System - File Organization
Purpose: Scalable file structure enabling unlimited content.
.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)
- SKILL.md: ≤500 lines, progressive disclosure, cross-references
- modules/: Topic-focused, no limits, self-contained
- examples.md: Copy-paste ready, commented
- reference.md: API docs, resources
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
Advanced Implementation (10+ minutes)
Cross-Module Integration
TRUST 5 + SPEC-First TDD:
spec = Task(subagent_type="workflow-spec", prompt="SPEC with TRUST 5")
impl = Task(subagent_type="workflow-tdd", prompt="≥85% coverage",
context={"spec": spec, "quality_gates": ["TRUST5"]})
validation = Task(subagent_type="core-quality", prompt="Validate TRUST 5",
context={"implementation": impl})
Token-Optimized Delegation:
spec = Task(subagent_type="workflow-spec", prompt="Generate SPEC")
execute_clear()
results = await Promise.all([
Task(subagent_type="backend-expert", prompt="Backend", context={"spec_id": spec.id}),
Task(subagent_type="frontend-expert", prompt="Frontend", context={"spec_id": spec.id})
])
Task(subagent_type="workflow-docs", prompt="Docs", context={"results": results})
Progressive Agent Workflows:
quick = Task(subagent_type="debug-helper", prompt="Quick diagnosis")
if quick.complexity == "high":
detailed = Task(subagent_type="debug-helper", prompt="Detailed analysis")
if detailed.requires_expert:
expert = Task(subagent_type="security-expert", prompt="Deep dive")
Quality Validation
def validate_execution_requirements(task, context):
return all([
validate_security_clearance(task),
validate_resource_availability(context),
validate_quality_standards(task),
validate_permission_compliance(task)
])
def validate_execution_results(result, task):
validations = [
validate_output_quality(result),
validate_security_compliance(result),
validate_test_coverage(result),
validate_documentation_completeness(result)
]
if not all(validations):
raise QualityGateError("Quality gate failures")
return True
Error Handling
try:
result = Task(subagent_type="backend-expert", prompt="Complex task")
except AgentExecutionError as e:
analysis = Task(subagent_type="debug-helper", prompt=f"Analyze: {e}")
if analysis.issue == "complexity":
results = await Promise.all([
Task(subagent_type="backend-expert", prompt="Subtask 1"),
Task(subagent_type="backend-expert", prompt="Subtask 2")
])
if token_usage > 150_000:
execute_clear()
context = {"spec_id": current_spec.id, "phase_results": summarize(previous_results)}
Task(subagent_type="next-agent", prompt="Continue", context=context)
Works Well With
- 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
- moai-cc-claude-md - CLAUDE.md with foundation patterns
- moai-cc-configuration - Config with TRUST 5
- moai-cc-memory - Token optimization
- moai-core-ask-user-questions - User clarification
- moai-context7-integration - MCP integration
- /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):
Quick Decision Matrix
| Scenario | Primary Principle | Supporting |
|---|
| New agent | TRUST 5 + Delegation | Token, modular |
| New skill | Progressive + Modular | TRUST 5, token |
| Workflow | Delegation | SPEC-First, token |
| Quality | TRUST 5 | SPEC-First |
| Budget | Token Optimization | Progressive, modular |
| Docs | Progressive + Modular | Token |
Version: 2.2.0
Last Updated: 2025-11-26
Status: Active (496 lines, within 500-line limit)
Ocupaciones relacionadasSOC
Basado en la clasificación ocupacional SOC
