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moai-alfred-clone-pattern Enterprise Master-Clone pattern implementation guide for complex multi-step tasks with full project context, autonomous delegation, parallel processing, and intelligent task distribution; activates for large-scale migrations, complex refactoring, parallel exploration, architecture restructuring, and multi-file transformations
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name moai-alfred-clone-pattern version 4.0.0 created 2025-11-05T00:00:00.000Z updated 2025-11-12T00:00:00.000Z status stable description Enterprise Master-Clone pattern implementation guide for complex multi-step tasks with full project context, autonomous delegation, parallel processing, and intelligent task distribution; activates for large-scale migrations, complex refactoring, parallel exploration, architecture restructuring, and multi-file transformations keywords ["clone-pattern","master-clone","delegation","multi-step","parallel-processing","autonomous-agents","task-distribution","project-context","complex-workflows","enterprise-delegation"] allowed-tools ["Read","Bash","Task"]
Enterprise Master-Clone Pattern Skill v4.0.0
Skill Metadata
Field Value Skill Name moai-alfred-clone-pattern Version 4.0.0 Enterprise (2025-11-12) Allowed tools Read, Bash, Task Auto-load On demand for complex multi-step tasks Tier Alfred (Orchestration) Lines of Content 900+ with 12+ enterprise examples Progressive Disclosure 3-level (quick-start, patterns, advanced)
What It Does
Provides comprehensive guidance for Alfred's Master-Clone pattern - a delegation mechanism where Alfred creates autonomous clones (Task-delegated agents) to handle complex multi-step tasks that don't require domain-specific expertise but benefit from:
Full project context and codebase understanding
Parallel processing capabilities
Independent decision-making
Comprehensive state tracking
When to Use (Decision Framework)
Use Clone Pattern when:
Task requires 5+ sequential steps OR affects 100+ files
No domain-specific expertise needed (not UI, Backend, DB, Security, ML)
Task is complex with high uncertainty
Parallel processing would be beneficial
Full project context is required for optimal decisions
Task can run autonomously without continuous user input
Examples :
Large-scale migrations (v0.14.0 → v0.15.2 affecting 200 files)
Refactoring across many files (100+ imports, API changes)
Parallel exploration/evaluation tasks
Complex architecture restructuring
Bulk file transformations with context-aware logic
Schema migrations affecting multiple services
Dependency upgrade cascades
DON'T use Clone Pattern when:
Domain expertise needed (use specialist agents instead)
Task < 5 steps (direct execution is faster)
Quick yes/no decision (use AskUserQuestion)
Single file modification (use tdd-implementer)
Key Concepts
Master-Clone Architecture Master Agent (Alfred)
↓ Creates with Task()
Clone Agent #1 Clone Agent #2 Clone Agent #3
(Parallel execution with shared context)
↓ ↓ ↓
[Exploration] [Analysis] [Implementation]
↓ ↓ ↓
Results aggregation & synthesis by Master
↓
User presentation + next steps
Analyze task scope and complexity
Decompose into independent parallel work
Create clones with appropriate context
Aggregate and synthesize results
Present findings to user
Execute assigned sub-task autonomously
Use full project context for intelligent decisions
Track state and progress
Report findings with evidence
Handle errors gracefully
3-Level Architecture
Level 1: Simple Parallel Task Scenario : Explore multiple implementation approaches in parallel
const clones = [
Task ({
description : "Explore PostgreSQL implementation for user persistence" ,
prompt : "Analyze PostgreSQL libraries, schema design, migration strategy. Provide pros/cons and code examples."
}),
Task ({
description : "Explore MongoDB implementation for user persistence" ,
prompt : "Analyze MongoDB libraries, document schema, migration strategy. Provide pros/cons and code examples."
}),
Task ({
description : "Explore Supabase implementation for user persistence" ,
prompt : "Analyze Supabase SDK, schema design, migration strategy. Provide pros/cons and code examples."
})
];
const [postgresAnalysis, mongoAnalysis, supabaseAnalysis] = await Promise .all (clones);
const comparison = {
options : [postgresAnalysis, mongoAnalysis, supabaseAnalysis],
recommendation : selectBestOption (clones),
tradeoffs : analyzeTradeoffs (clones)
};
Level 2: Sequential Dependent Tasks Scenario : Complex migration where later steps depend on earlier analysis
const analysisResult = await Task ({
description : "Analyze v0.14.0 codebase structure" ,
prompt : "Scan project for all imports of 'old-api'. Document usage patterns, edge cases, and dependencies. Provide summary with file-by-file breakdown."
});
const planResult = await Task ({
description : "Plan migration strategy from v0.14.0 to v0.15.2" ,
prompt : `Using this analysis: ${analysisResult} \n\nCreate a step-by-step migration plan with:\n- Phased approach (phase 1, 2, 3)\n- Risk mitigation\n- Testing strategy\n- Rollback procedure`
});
const migrationResult = await Task ({
description : "Execute v0.14.0 → v0.15.2 migration" ,
prompt : `Using this plan: ${planResult} \n\nExecute the migration:\n- Update imports\n- Modify APIs\n- Update tests\n- Verify compatibility`
});
const validationResult = await Task ({
description : "Validate migration completeness" ,
prompt : `Verify migration:\n- All imports updated\n- No breaking changes\n- Tests passing\n- Performance metrics maintained`
});
return {
analysis : analysisResult,
plan : planResult,
migration : migrationResult,
validation : validationResult,
status : validationResult.passed ? "SUCCESS" : "NEEDS_REVIEW"
};
Level 3: Hybrid Parallel + Sequential (Advanced) Scenario : Large refactoring with parallel analysis, synchronized implementation
const [apiAnalysis, dbAnalysis, authAnalysis] = await Promise .all ([
Task ({ description : "Analyze API layer usage..." , prompt : "..." }),
Task ({ description : "Analyze DB layer usage..." , prompt : "..." }),
Task ({ description : "Analyze Auth layer usage..." , prompt : "..." })
]);
const [apiRefactor, dbRefactor, authRefactor] = await Promise .all ([
Task ({
description : "Refactor API layer" ,
prompt : `Based on analysis:\n${apiAnalysis} \n\nRefactor API with:\n- New patterns\n- Tests\n- Documentation`
}),
Task ({
description : "Refactor DB layer" ,
prompt : `Based on analysis:\n${dbAnalysis} \n\nRefactor DB with:\n- Schema updates\n- Migration scripts\n- Tests`
}),
Task ({
description : "Refactor Auth layer" ,
prompt : `Based on analysis:\n${authAnalysis} \n\nRefactor Auth with:\n- New strategy\n- Migration\n- Tests`
})
]);
const integrationResult = await Task ({
description : "Validate refactored layer integration" ,
prompt : `Verify all refactored layers work together:\n- API uses new DB patterns\n- Auth integrates with API\n- No breaking changes\n- All tests passing`
});
return {
phase1 : { apiAnalysis, dbAnalysis, authAnalysis },
phase2 : { apiRefactor, dbRefactor, authRefactor },
phase3 : integrationResult,
status : "COMPLETE"
};
Best Practices
DO
Define clear sub-tasks : Each clone should have a specific, measurable goalProvide full context : Include project structure, existing patterns, constraintsUse sequential when needed : Depend on earlier results when necessaryValidate results : Always verify clone outputs before proceedingDocument findings : Track what each clone discoveredHandle failures gracefully : Plan for individual clone failuresAggregate intelligently : Synthesize parallel results into coherent analysis
DON'T
Over-parallelize : Creating 20 clones is overkill (use 2-5)Under-specify tasks : Vague descriptions lead to mediocre resultsIgnore dependencies : Force sequential when tasks actually dependSkip validation : Trust but verify clone outputsLose context : Always include relevant project informationCreate circular dependencies : Avoid Task A waiting on Task B waiting on Task A
Implementation Patterns
Pattern 1: Exploration with Synthesis
const explorations = await Promise .all (approaches.map (approach =>Task ({
description : `Explore ${approach.name} approach` ,
prompt : `Research ${approach.name} ...\nProvide: pros, cons, code example, learning curve`
})
));
return {
comparison : createComparisonTable (explorations),
recommendation : selectBestApproach (explorations),
decisionRationale : explainDecision (explorations)
};
Pattern 2: Phased Migration
const analysis = await analyzeCurrentState ();
const plan = await planMigration (analysis);
const implementation = await implementMigration (plan);
const validation = await validateMigration (implementation);
return { analysis, plan, implementation, validation };
Pattern 3: Distributed Refactoring
const fileGroups = splitFilesIntoGroups (files, 5 );
const refactorResults = await Promise .all (
fileGroups.map (group =>Task ({
description : `Refactor files: ${group.join(", " )} ` ,
prompt : `Refactor these files using new patterns:\n${group.join("\n" )} `
})
)
);
return validateIntegration (refactorResults);
When NOT to Use (Anti-Patterns) Scenario Why Use Instead Single file change Too much overhead Direct tdd-implementer 2-3 quick steps Sequential simpler Direct execution Domain expertise required Needs specialist Specialist agent (security, DB, etc.) Real-time interaction Clones run independently Interactive agent Simple query Overkill complexity Direct lookup
Related Skills
moai-alfred-agent-guide (Agent architecture & delegation)moai-alfred-task-decomposition (Breaking down complex tasks)moai-essentials-refactor (Refactoring patterns & examples)
For detailed API specifications : reference.md For real-world examples : examples.md Last Updated : 2025-11-12Status : Production Ready (Enterprise v4.0.0)
