Skill v4.0.0
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name: "moai-alfred-practices" version: "4.0.0" created: 2025-11-05 updated: 2025-11-12 status: stable description: Enterprise practical workflows, context engineering strategies, JIT (Just-In-Time) retrieval optimization, real-world execution examples, debugging patterns, and moai-adk workflow mastery; activates for workflow pattern learning, context optimization, debugging issue resolution, feature implementation end-to-end, and team knowledge transfer keywords: ['workflow-patterns', 'context-engineering', 'jit-retrieval', 'agent-usage', 'debugging-patterns', 'feature-implementation', 'practical-examples', 'moai-adk-mastery', 'knowledge-transfer', 'enterprise-workflows'] allowed-tools:
- Read
- Glob
- Bash
- WebFetch
- mcp__context7__resolve-library-id
- mcp__context7__get-library-docs
Enterprise Practical Workflows & Context Engineering v4.0.0
Skill Metadata
| Field | Value | |
|---|---|---|
| Skill Name | moai-alfred-practices | |
| Version | 4.0.0 Enterprise (2025-11-12) | |
| Focus | Practical execution patterns, real-world scenarios | |
| Auto-load | When workflow guidance or debugging help needed | |
| Included Patterns | 15+ real-world scenarios | |
| Lines of Content | 950+ with 20+ production examples | |
| Progressive Disclosure | 3-level (quick-patterns, scenarios, advanced) |
What It Does
Provides practical workflows, context engineering strategies, real-world execution examples, and debugging solutions for moai-adk. Covers JIT context management, efficient agent usage, SPEC→TDD→Sync execution, and common problem resolution.
JIT (Just-In-Time) Context Strategy
Principle: Load Only What's Needed Now
Traditional (overload):Load entire codebase→ Context window fills immediately→ Limited reasoning capacity→ Slow, inefficientJIT (optimized):Load core entry points→ Identify specific function/module→ Load only that section→ Cache in thread context→ Reuse for related tasks→ Minimal context waste
Practice 1: Core Module Mapping
# 1. Get high-level structurefind src/ -type f -name "*.py" | wc -l# Output: 145 files total# 2. Identify entry points (only 3-5 files)find src/ -name "__main__.py" -o -name "main.py" -o -name "run.py"# 3. Load entry point + immediate dependenciesGlob("src/{**/}*.py")# Load only files referenced by entry point# 4. Cache in Task() context for reuseTask(prompt="Task 1 using mapped modules")Task(prompt="Task 2 reuses cached context")
Practice 2: Dependency Tree Navigation
Project Root├─ src/│ ├─ __init__.py ← Entry point #1│ ├─ main.py ← Entry point #2│ ├─ core/│ │ ├─ domain.py ← Core models│ │ ├─ repository.py ← Data access│ │ └─ service.py ← Business logic│ └─ api/│ ├─ routes.py ← API endpoints│ └─ handlers.py ← Request handlersLoad strategy:1. Load main.py + __init__.py (entry points)2. When modifying API → Load api/ subtree3. When fixing business logic → Load core/service.py4. Cache all loaded files in context5. Share context between related tasks
Practice 3: Context Reuse Across Tasks
# Task 1: Understand module structureanalysis = Task({prompt="Map src/ directory structure, identify entry points, list dependencies"})# Task 2: Reuse analysis for implementationimplementation = Task({prompt=f"""Using this structure:{analysis}Now implement feature X..."""})# Task 3: Reuse analysis for testingtesting = Task({prompt=f"""Using this structure:{analysis}Write tests for feature X..."""})# Result: No re-mapping, efficient context reuse
SPEC → TDD → Sync Execution Pattern
Step 1: Create SPEC with /alfred:1-plan
/alfred:1-plan "Add user authentication with JWT"# This creates:# .moai/specs/SPEC-042/spec.md (full requirements)# feature/SPEC-042 (git branch)# Track with TodoWrite
Step 2: Implement with /alfred:2-run SPEC-042
RED: Test agent writes failing tests↓GREEN: Implementer agent creates minimal code↓REFACTOR: Quality agent improves code↓Repeat TDD cycle for each feature component↓All tests passing, coverage ≥85%
Step 3: Sync with /alfred:3-sync auto SPEC-042
Updates:✓ Documentation✓ Test coverage metrics✓ Creates PR to develop✓ Auto-validation of quality gates
Debugging Pattern: Issue → Root Cause → Fix
Step 1: Triage & Understand
Error message: "Cannot read property 'user_id' of undefined"Questions:- When does it occur? (always, intermittently, specific scenario)- Which code path? (which endpoint/function)- What's the state? (what data led to this)- What changed recently? (revert to narrow down)
Step 2: Isolate Root Cause
# Method 1: Binary search# Is it in API layer? → Yes# Is it in route handler? → No# Is it in service layer? → Yes# Is it in this function? → Narrow down# Method 2: Add logginglogger.debug(f"user_id = {user_id}") # Check where it becomes undefined# Method 3: Test locally# Reproduce with minimal example# Add breakpoint in debugger# Step through execution
Step 3: Fix with Tests
# RED: Write failing testdef test_handles_missing_user_id():"""Should handle case when user_id is undefined."""assert get_user(None) raises ValueError# GREEN: Minimal fixdef get_user(user_id):if not user_id:raise ValueError("user_id required")return fetch_user(user_id)# REFACTOR: Improvedef get_user(user_id: int) -> User:"""Get user by ID.Args:user_id: User identifierRaises:ValueError: If user_id is None or invalid"""if not user_id or user_id <= 0:raise ValueError(f"Invalid user_id: {user_id}")return self.user_repo.find(user_id)
5 Real-World Scenarios
Scenario 1: Feature Implementation (2-3 hours)
1. Create SPEC: /alfred:1-plan "Add user dashboard"2. Clarify details: AskUserQuestion (which data to show?)3. Implement: /alfred:2-run SPEC-XXX (TDD cycle)4. Document: /alfred:3-sync auto SPEC-XXX5. Result: Production-ready feature
Scenario 2: Bug Investigation (1-2 hours)
1. Reproduce: Create minimal test case2. Isolate: Narrow down affected code3. Debug: Add logging, trace execution4. Fix: TDD RED→GREEN→REFACTOR5. Validate: Ensure tests pass, regression tests
Scenario 3: Large Refactoring (4-8 hours)
1. Analyze: Map current code structure2. Plan: Design new structure with trade-offs3. Clone pattern: Create autonomous agents for parallel refactoring4. Integrate: Verify all pieces work together5. Test: Comprehensive test coverage
Scenario 4: Performance Optimization (2-4 hours)
1. Profile: Identify bottleneck with profiler2. Analyze: Understand performance characteristics3. Design: Plan optimization approach4. Implement: TDD RED→GREEN→REFACTOR5. Validate: Benchmark before/after
Scenario 5: Multi-Team Coordination (ongoing)
1. SPEC clarity: AskUserQuestion for ambiguous requirements2. Agent routing: Delegate to specialist teams3. Progress tracking: TodoWrite for coordination4. Integration: Verify components work together5. Documentation: Central SPEC as source of truth
Context Budget Optimization
Typical project context:- Config files: ~50 tokens- .moai/ structure: ~100 tokens- Entry points (3-5 files): ~500 tokens- SPEC document: ~200 tokens→ Total: ~850 tokens per sessionReusable context:- Load once per session- Share across 5-10 tasks- Saves: 3,500-8,500 tokens per session- Result: More reasoning capacity
Best Practices
DO
- ✅ Load entry points first (3-5 files)
- ✅ Identify dependencies before deep dive
- ✅ Reuse analyzed context across tasks
- ✅ Cache intermediate results in Task context
- ✅ Follow SPEC → TDD → Sync workflow
- ✅ Track progress with TodoWrite
- ✅ Ask for clarification (AskUserQuestion)
- ✅ Test before declaring done
DON'T
- ❌ Load entire codebase at once
- ❌ Reanalyze same code multiple times
- ❌ Skip SPEC clarification (causes rework)
- ❌ Write code without tests
- ❌ Ignore error messages
- ❌ Assume context understanding
- ❌ Skip documentation updates
- ❌ Commit without running tests
Related Skills
moai-alfred-agent-guide(Agent orchestration patterns)moai-alfred-clone-pattern(Complex task delegation)moai-essentials-debug(Debugging techniques)
For detailed workflow examples: reference.md For real-world scenarios: examples.md Last Updated: 2025-11-12 Status: Production Ready (Enterprise v4.0.0)