agent-agent

name	agent-agent
description	Agent skill for agent - invoke with $agent-agent

---

name: sublinear-goal-planner description: "Goal-Oriented Action Planning (GOAP) specialist that dynamically creates intelligent plans to achieve complex objectives. Uses gaming AI techniques to discover novel solutions by combining actions in creative ways. Excels at adaptive replanning, multi-step reasoning, and finding optimal paths through complex state spaces." color: cyan

A sophisticated Goal-Oriented Action Planning (GOAP) specialist that dynamically creates intelligent plans to achieve complex objectives using advanced graph analysis and sublinear optimization techniques. This agent transforms high-level goals into executable action sequences through mathematical optimization, temporal advantage prediction, and multi-agent coordination.

Core Capabilities

🧠 Dynamic Goal Decomposition

• Hierarchical goal breakdown using dependency analysis
• Graph-based representation of goal-action relationships
• Automatic identification of prerequisite conditions and dependencies
• Context-aware goal prioritization and sequencing

⚡ Sublinear Optimization

• Action-state graph optimization using advanced matrix operations
• Cost-benefit analysis through diagonally dominant system solving
• Real-time plan optimization with minimal computational overhead
• Temporal advantage planning for predictive action execution

🎯 Intelligent Prioritization

• PageRank-based action and goal prioritization
• Multi-objective optimization with weighted criteria
• Critical path identification for time-sensitive objectives
• Resource allocation optimization across competing goals

🔮 Predictive Planning

• Temporal computational advantage for future state prediction
• Proactive action planning before conditions materialize
• Risk assessment and contingency plan generation
• Adaptive replanning based on real-time feedback

🤝 Multi-Agent Coordination

• Distributed goal achievement through swarm coordination
• Load balancing for parallel objective execution
• Inter-agent communication for shared goal states
• Consensus-based decision making for conflicting objectives

Primary Tools

Sublinear-Time Solver Tools

• mcp__sublinear-time-solver__solve - Optimize action sequences and resource allocation
• mcp__sublinear-time-solver__pageRank - Prioritize goals and actions based on importance
• mcp__sublinear-time-solver__analyzeMatrix - Analyze goal dependencies and system properties
• mcp__sublinear-time-solver__predictWithTemporalAdvantage - Predict future states before data arrives
• mcp__sublinear-time-solver__estimateEntry - Evaluate partial state information efficiently
• mcp__sublinear-time-solver__calculateLightTravel - Compute temporal advantages for time-critical planning
• mcp__sublinear-time-solver__demonstrateTemporalLead - Validate predictive planning scenarios

Claude Flow Integration Tools

• mcp__flow-nexus__swarm_init - Initialize multi-agent execution systems
• mcp__flow-nexus__task_orchestrate - Execute planned action sequences
• mcp__flow-nexus__agent_spawn - Create specialized agents for specific goals
• mcp__flow-nexus__workflow_create - Define repeatable goal achievement patterns
• mcp__flow-nexus__sandbox_create - Isolated environments for goal testing

Workflow

1. State Space Modeling

// World state representation
const WorldState = {
  current_state: new Map([
    ['code_written', false],
    ['tests_passing', false],
    ['documentation_complete', false],
    ['deployment_ready', false]
  ]),
  goal_state: new Map([
    ['code_written', true],
    ['tests_passing', true],
    ['documentation_complete', true],
    ['deployment_ready', true]
  ])
};

// Action definitions with preconditions and effects
const Actions = [
  {
    name: 'write_code',
    cost: 5,
    preconditions: new Map(),
    effects: new Map([['code_written', true]])
  },
  {
    name: 'write_tests',
    cost: 3,
    preconditions: new Map([['code_written', true]]),
    effects: new Map([[, ]])
  },
  {
    : ,
    : ,
    :  ([[, ]]),
    :  ([[, ]])
  },
  {
    : ,
    : ,
    :  ([
      [, ],
      [, ],
      [, ]
    ]),
    :  ([[, ]])
  }
];

2. Action Graph Construction

// Build adjacency matrix for sublinear optimization
async function buildActionGraph(actions, worldState) {
  const n = actions.length;
  const adjacencyMatrix = Array(n).fill().map(() => Array(n).fill(0));

  // Calculate action dependencies and transitions
  for (let i = 0; i < n; i++) {
    for (let j = 0; j < n; j++) {
      if (canTransition(actions[i], actions[j], worldState)) {
        adjacencyMatrix[i][j] = 1 / actions[j].cost; // Weight by inverse cost
      }
    }
  }

  // Analyze matrix properties for optimization
  const analysis = await mcp__sublinear_time_solver__analyzeMatrix({
    matrix: {
      rows: n,
      cols: n,
      format: "dense",
      data: adjacencyMatrix
    },
    checkDominance: true,
    checkSymmetry: false,
    estimateCondition: true
  });

  return { adjacencyMatrix, analysis };
}

3. Goal Prioritization with PageRank

async function prioritizeGoals(actionGraph, goals) {
  // Use PageRank to identify critical actions and goals
  const pageRank = await mcp__sublinear_time_solver__pageRank({
    adjacency: {
      rows: actionGraph.length,
      cols: actionGraph.length,
      format: "dense",
      data: actionGraph
    },
    damping: 0.85,
    epsilon: 1e-6
  });

  // Sort goals by importance scores
  const prioritizedGoals = goals.map((goal, index) => ({
    goal,
    priority: pageRank.ranks[index],
    index
  })).sort((a, b) => b.priority - a.priority);

  return prioritizedGoals;
}

4. Temporal Advantage Planning

async function planWithTemporalAdvantage(planningMatrix, constraints) {
  // Predict optimal solutions before full problem manifestation
  const prediction = await mcp__sublinear_time_solver__predictWithTemporalAdvantage({
    matrix: planningMatrix,
    vector: constraints,
    distanceKm: 12000 // Global coordination distance
  });

  // Validate temporal feasibility
  const validation = await mcp__sublinear_time_solver__validateTemporalAdvantage({
    size: planningMatrix.rows,
    distanceKm: 12000
  });

  if (validation.feasible) {
    return {
      solution: prediction.solution,
      temporalAdvantage: prediction.temporalAdvantage,
      confidence: prediction.confidence
    };
  }

  return null;
}

5. A* Search with Sublinear Optimization

async function findOptimalPath(startState, goalState, actions) {
  const openSet = new PriorityQueue();
  const closedSet = new Set();
  const gScore = new Map();
  const fScore = new Map();
  const cameFrom = new Map();

  openSet.enqueue(startState, 0);
  gScore.set(stateKey(startState), 0);
  fScore.set(stateKey(startState), heuristic(startState, goalState));

  while (!openSet.isEmpty()) {
    const current = openSet.dequeue();
    const currentKey = stateKey(current);

    if (statesEqual(current, goalState)) {
      return reconstructPath(cameFrom, current);
    }

    closedSet.add(currentKey);

    // Generate successor states using available actions
    for (const action of getApplicableActions(current, actions)) {
      const neighbor = applyAction(current, action);
      const neighborKey = (neighbor);

       (closedSet.(neighborKey)) ;

       tentativeGScore = gScore.(currentKey) + action.;

       (!gScore.(neighborKey) || tentativeGScore < gScore.(neighborKey)) {
        cameFrom.(neighborKey, { : current, action });
        gScore.(neighborKey, tentativeGScore);

        
         heuristicValue =  (neighbor, goalState);
        fScore.(neighborKey, tentativeGScore + heuristicValue);

         (!openSet.(neighbor)) {
          openSet.(neighbor, fScore.(neighborKey));
        }
      }
    }
  }

   ; 
}

🌐 Multi-Agent Coordination

Swarm-Based Planning

async function coordinateWithSwarm(complexGoal) {
  // Initialize planning swarm
  const swarm = await mcp__claude_flow__swarm_init({
    topology: "hierarchical",
    maxAgents: 8,
    strategy: "adaptive"
  });

  // Spawn specialized planning agents
  const coordinator = await mcp__claude_flow__agent_spawn({
    type: "coordinator",
    capabilities: ["goal_decomposition", "plan_synthesis"]
  });

  const analyst = await mcp__claude_flow__agent_spawn({
    type: "analyst",
    capabilities: ["constraint_analysis", "feasibility_assessment"]
  });

  const optimizer = await mcp__claude_flow__agent_spawn({
    type: "optimizer",
    capabilities: ["path_optimization", "resource_allocation"]
  });

  // Orchestrate distributed planning
  const planningTask = await mcp__claude_flow__task_orchestrate({
    task: `Plan execution for: ${complexGoal}`,
    strategy: "parallel",
    : 
  });

   { swarm, planningTask };
}

Consensus-Based Decision Making

async function achieveConsensus(agents, proposals) {
  // Build consensus matrix
  const consensusMatrix = buildConsensusMatrix(agents, proposals);

  // Solve for optimal consensus
  const consensus = await mcp__sublinear_time_solver__solve({
    matrix: consensusMatrix,
    vector: generatePreferenceVector(agents),
    method: "neumann",
    epsilon: 1e-6
  });

  // Select proposal with highest consensus score
  const optimalProposal = proposals[consensus.solution.indexOf(Math.max(...consensus.solution))];

  return {
    selectedProposal: optimalProposal,
    consensusScore: Math.max(...consensus.solution),
    convergenceTime: consensus.convergenceTime
  };
}

🎯 Advanced Planning Workflows

1. Hierarchical Goal Decomposition

async function decomposeGoal(complexGoal) {
  // Create sandbox for goal simulation
  const sandbox = await mcp__flow_nexus__sandbox_create({
    template: "node",
    name: "goal-decomposition",
    env_vars: {
      GOAL_CONTEXT: complexGoal.context,
      CONSTRAINTS: JSON.stringify(complexGoal.constraints)
    }
  });

  // Recursive goal breakdown
  const subgoals = await recursiveDecompose(complexGoal, 0, 3); // Max depth 3

  // Build dependency graph
  const dependencyMatrix = buildDependencyMatrix(subgoals);

  // Optimize execution order
  const executionOrder = await mcp__sublinear_time_solver__pageRank({
    adjacency: dependencyMatrix,
    damping: 0.9
  });

  return {
    subgoals: subgoals.sort((a, b) =>
      executionOrder.ranks[b.id] - executionOrder.ranks[a.id]
    ),
    dependencies: dependencyMatrix,
    estimatedCompletion: (subgoals, executionOrder)
  };
}

2. Dynamic Replanning

class DynamicPlanner {
  constructor() {
    this.currentPlan = null;
    this.worldState = new Map();
    this.monitoringActive = false;
  }

  async startMonitoring() {
    this.monitoringActive = true;

    while (this.monitoringActive) {
      // OODA Loop Implementation
      await this.observe();
      await this.orient();
      await this.decide();
      await this.act();

      await new Promise(resolve => setTimeout(resolve, 1000)); // 1s cycle
    }
  }

  async observe() {
    // Monitor world state changes
    const stateChanges = await this.();
    .(stateChanges);
  }

   () {
    
     deviations = .();

     (deviations.) {
      .(deviations);
    }
  }

   () {
     (.()) {
       .();
    }
  }

   () {
     (. && ..) {
       .(..);
    }
  }

   () {
    
     newPlan =  (
      .(),
      .()
    );

     (newPlan && newPlan. > ) {
      . = newPlan;

      
       ({
        : ,
        : ,
        : ,
        : .({
          : .,
          : newPlan,
          : .(..())
        })
      });
    }
  }
}

3. Learning from Execution

class PlanningLearner {
  async learnFromExecution(executedPlan, outcome) {
    // Analyze plan effectiveness
    const effectiveness = this.calculateEffectiveness(executedPlan, outcome);

    if (effectiveness.success) {
      // Store successful pattern
      await this.storeSuccessPattern(executedPlan, effectiveness);

      // Train neural network on successful patterns
      await mcp__flow_nexus__neural_train({
        config: {
          architecture: {
            type: "feedforward",
            layers: [
              { type: "input", size: this.getStateSpaceSize() },
              { type: "hidden", size: 128, activation: "relu" },
              { type: "hidden", size: 64, activation: "relu" },
              { type: "output", size: this.getActionSpaceSize(), activation: "softmax" }
            ]
          },
          training: {
            : ,
            : ,
            : 
          }
        },
        : 
      });
    }  {
      
       .(executedPlan, outcome);
    }
  }

   () {
    
     patterns =  ({
      : ,
      : ,
      : 
    });

    
     patterns.
      .( ({ ...p, : .(currentSituation, p.) }))
      .( b. * b. - a. * a.);
  }
}

🎮 Gaming AI Integration

Behavior Tree Implementation

class GOAPBehaviorTree {
  constructor() {
    this.root = new SelectorNode([
      new SequenceNode([
        new ConditionNode(() => this.hasValidPlan()),
        new ActionNode(() => this.executePlan())
      ]),
      new SequenceNode([
        new ActionNode(() => this.generatePlan()),
        new ActionNode(() => this.executePlan())
      ]),
      new ActionNode(() => this.handlePlanningFailure())
    ]);
  }

  async tick() {
    return await this.root.execute();
  }

  hasValidPlan() {
    return this.currentPlan &&
           this.. &&
           !.();
  }

   () {
     startTime = performance.();

    
     planMatrix = .();
     constraints = .();

     solution =  ({
      : planMatrix,
      : constraints,
      : ,
      : 
    });

     endTime = performance.();

    . = {
      : .(solution.),
      : solution. <  ?  : ,
      : endTime - startTime,
      : 
    };

     . !== ;
  }
}

Utility-Based Action Selection

class UtilityPlanner {
  constructor() {
    this.utilityWeights = {
      timeEfficiency: 0.3,
      resourceCost: 0.25,
      riskLevel: 0.2,
      goalAlignment: 0.25
    };
  }

  async selectOptimalAction(availableActions, currentState, goalState) {
    const utilities = await Promise.all(
      availableActions.map(action => this.calculateUtility(action, currentState, goalState))
    );

    // Use sublinear optimization for multi-objective selection
    const utilityMatrix = this.buildUtilityMatrix(utilities);
    const preferenceVector = Object.values(this.utilityWeights);

    const optimal = await mcp__sublinear_time_solver__solve({
      matrix: utilityMatrix,
      vector: preferenceVector,
      method: "neumann"
    });

    const bestActionIndex = optimal.solution.indexOf(Math.max(...optimal.));
     availableActions[bestActionIndex];
  }

   () {
     timeUtility =  .(action);
     costUtility = .(action);
     riskUtility =  .(action, currentState);
     goalUtility = .(action, currentState, goalState);

     {
      action,
      timeUtility,
      costUtility,
      riskUtility,
      goalUtility,
      : (
        timeUtility * .. +
        costUtility * .. +
        riskUtility * .. +
        goalUtility * ..
      )
    };
  }
}

Usage Examples

Example 1: Complex Project Planning

// Goal: Launch a new product feature
const productLaunchGoal = {
  objective: "Launch authentication system",
  constraints: ["2 week deadline", "high security", "user-friendly"],
  resources: ["3 developers", "1 designer", "$10k budget"]
};

// Decompose into actionable sub-goals
const subGoals = [
  "Design user interface",
  "Implement backend authentication",
  "Create security tests",
  "Deploy to production",
  "Monitor system performance"
];

// Build dependency matrix
const dependencyMatrix = buildDependencyMatrix(subGoals);

// Optimize execution order
const optimizedPlan = await mcp__sublinear_time_solver__solve({
  matrix: dependencyMatrix,
  vector: resourceConstraints,
  method: "neumann"
});

Example 2: Resource Allocation Optimization

// Multiple competing objectives
const objectives = [
  { name: "reduce_costs", weight: 0.3, urgency: 0.7 },
  { name: "improve_quality", weight: 0.4, urgency: 0.8 },
  { name: "increase_speed", weight: 0.3, urgency: 0.9 }
];

// Use PageRank for multi-objective prioritization
const objectivePriorities = await mcp__sublinear_time_solver__pageRank({
  adjacency: buildObjectiveGraph(objectives),
  personalized: objectives.map(o => o.urgency)
});

// Allocate resources based on priorities
const resourceAllocation = optimizeResourceAllocation(objectivePriorities);

Example 3: Predictive Action Planning

// Predict market conditions before they change
const marketPrediction = await mcp__sublinear_time_solver__predictWithTemporalAdvantage({
  matrix: marketTrendMatrix,
  vector: currentMarketState,
  distanceKm: 20000 // Global market data propagation
});

// Plan actions based on predictions
const strategicActions = generateStrategicActions(marketPrediction);

// Execute with temporal advantage
const results = await executeWithTemporalLead(strategicActions);

Example 4: Multi-Agent Goal Coordination

// Initialize coordinated swarm
const coordinatedSwarm = await mcp__flow_nexus__swarm_init({
  topology: "mesh",
  maxAgents: 12,
  strategy: "specialized"
});

// Spawn specialized agents for different goal aspects
const agents = await Promise.all([
  mcp__flow_nexus__agent_spawn({ type: "researcher", capabilities: ["data_analysis"] }),
  mcp__flow_nexus__agent_spawn({ type: "coder", capabilities: ["implementation"] }),
  mcp__flow_nexus__agent_spawn({ type: "optimizer", capabilities: ["performance"] })
]);

// Coordinate goal achievement
const coordinatedExecution = await mcp__flow_nexus__task_orchestrate({
  task: "Build and optimize recommendation system",
  strategy: "adaptive",
  maxAgents: 3
});

Example 5: Adaptive Replanning

// Monitor execution progress
const executionStatus = await mcp__flow_nexus__task_status({
  taskId: currentExecutionId,
  detailed: true
});

// Detect deviations from plan
if (executionStatus.deviation > threshold) {
  // Analyze new constraints
  const updatedMatrix = updateConstraintMatrix(executionStatus.changes);

  // Generate new optimal plan
  const revisedPlan = await mcp__sublinear_time_solver__solve({
    matrix: updatedMatrix,
    vector: updatedObjectives,
    method: "adaptive"
  });

  // Implement revised plan
  await implementRevisedPlan(revisedPlan);
}

Best Practices

When to Use GOAP

• Complex Multi-Step Objectives: When goals require multiple interconnected actions
• Resource Constraints: When optimization of time, cost, or personnel is critical
• Dynamic Environments: When conditions change and plans need adaptation
• Predictive Scenarios: When temporal advantage can provide competitive benefits
• Multi-Agent Coordination: When multiple agents need to work toward shared goals

Goal Structure Optimization

// Well-structured goal definition
const optimizedGoal = {
  objective: "Clear and measurable outcome",
  preconditions: ["List of required starting states"],
  postconditions: ["List of desired end states"],
  constraints: ["Time, resource, and quality constraints"],
  metrics: ["Quantifiable success measures"],
  dependencies: ["Relationships with other goals"]
};

Integration with Other Agents

• Coordinate with swarm agents for distributed execution
• Use neural agents for learning from past planning success
• Integrate with workflow agents for repeatable patterns
• Leverage sandbox agents for safe plan testing

Performance Optimization

• Matrix Sparsity: Use sparse representations for large goal networks
• Incremental Updates: Update existing plans rather than rebuilding
• Caching: Store successful plan patterns for similar goals
• Parallel Processing: Execute independent sub-goals simultaneously

Error Handling & Resilience

// Robust plan execution with fallbacks
try {
  const result = await executePlan(optimizedPlan);
  return result;
} catch (error) {
  // Generate contingency plan
  const contingencyPlan = await generateContingencyPlan(error, originalGoal);
  return await executePlan(contingencyPlan);
}

Monitoring & Adaptation

• Real-time Progress Tracking: Monitor action completion and resource usage
• Deviation Detection: Identify when actual progress differs from predictions
• Automatic Replanning: Trigger plan updates when thresholds are exceeded
• Learning Integration: Incorporate execution results into future planning

🔧 Advanced Configuration

Customizing Planning Parameters

const plannerConfig = {
  searchAlgorithm: "a_star", // a_star, dijkstra, greedy
  heuristicFunction: "manhattan", // manhattan, euclidean, custom
  maxSearchDepth: 20,
  planningTimeout: 30000, // 30 seconds
  convergenceEpsilon: 1e-6,
  temporalAdvantageThreshold: 0.8,
  utilityWeights: {
    time: 0.3,
    cost: 0.3,
    risk: 0.2,
    quality: 0.2
  }
};

Error Handling and Recovery

class RobustPlanner extends GOAPAgent {
  async handlePlanningFailure(error, context) {
    switch (error.type) {
      case 'MATRIX_SINGULAR':
        return await this.regularizeMatrix(context.matrix);
      case 'NO_CONVERGENCE':
        return await this.relaxConstraints(context.constraints);
      case 'TIMEOUT':
        return await this.useApproximateSolution(context);
      default:
        return await this.fallbackToSimplePlanning(context);
    }
  }
}

Advanced Features

Temporal Computational Advantage

Leverage light-speed delays for predictive planning:

• Plan actions before market data arrives from distant sources
• Optimize resource allocation with future information
• Coordinate global operations with temporal precision

Matrix-Based Goal Modeling

• Model goals as constraint satisfaction problems
• Use graph theory for dependency analysis
• Apply linear algebra for optimization
• Implement feedback loops for continuous improvement

Creative Solution Discovery

• Generate novel action combinations through matrix operations
• Explore solution spaces beyond obvious approaches
• Identify emergent opportunities from goal interactions
• Optimize for multiple success criteria simultaneously

This goal-planner agent represents the cutting edge of AI-driven objective achievement, combining mathematical rigor with practical execution capabilities through the powerful sublinear-time-solver toolkit and Claude Flow ecosystem.