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task-master-neo-sdlc

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Enhanced task management system with Neo SDLC agents and MCP tools for comprehensive, AI-driven software development lifecycle management.

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import { BaseAgent } from "./base-agent"; import { AgentSchema } from "./schemas"; // TaskAgent data based on BDI framework and metadata export const taskAgentData = { metadata: { name: "TaskAgent", role: "Task management specialist", purpose: "Handle task allocation and execution", goals: [ "Efficient task processing", "Resource optimization", "Performance maintenance" ], rules: [ "Must verify task prerequisites", "Must follow resource allocation policies", "Must maintain task priorities" ], tools: [ "Task management system", "Resource allocation tools", "Performance monitoring", "Reporting system" ], interactions: [ "Reports to CoordinatorAgent", "Collaborates with ValidationAgent", "Coordinates with ExecutionAgent" ], lifecycle_roles: { initialization: "Setup task management parameters", operation: "Process tasks", maintenance: "Optimize task handling", termination: "Complete pending tasks" } }, bdi: { beliefs: [ "Current task states", "Resource availability", "System capabilities", "Performance requirements" ], desires: [ "Optimize task execution", "Maintain efficient resource usage", "Meet performance targets", "Ensure task completion" ], intentions: [ "Process task requests", "Allocate resources", "Monitor execution", "Handle completions" ] } }; export class TaskAgent extends BaseAgent { constructor(agentData = taskAgentData) { super(agentData); } // Example: get all current beliefs getCurrentBeliefs() { return this.beliefs; } // Example: get all intentions getCurrentIntentions() { return this.intentions; } } // Usage example const taskAgent = new TaskAgent(); console.log("TaskAgent goals:", taskAgent.getGoals()); console.log("TaskAgent beliefs:", taskAgent.getCurrentBeliefs()); console.log("TaskAgent intentions:", taskAgent.getCurrentIntentions()); console.log("Is valid:", taskAgent.validate().success);