Agent Skills: agent-agent

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([['tests_passing', true]])
  },
  {
    name: 'write_documentation',
    cost: 2,
    preconditions: new Map([['code_written', true]]),
    effects: new Map([['documentation_complete', true]])
  },
  {
    name: 'deploy_application',
    cost: 4,
    preconditions: new Map([
      ['code_written', true],
      ['tests_passing', true],
      ['documentation_complete', true]
    ]),
    effects: new Map([['deployment_ready', true]])
  }
];

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 = stateKey(neighbor);

      if (closedSet.has(neighborKey)) continue;

      const tentativeGScore = gScore.get(currentKey) + action.cost;

      if (!gScore.has(neighborKey) || tentativeGScore < gScore.get(neighborKey)) {
        cameFrom.set(neighborKey, { state: current, action });
        gScore.set(neighborKey, tentativeGScore);

        // Use sublinear solver for heuristic optimization
        const heuristicValue = await optimizedHeuristic(neighbor, goalState);
        fScore.set(neighborKey, tentativeGScore + heuristicValue);

        if (!openSet.contains(neighbor)) {
          openSet.enqueue(neighbor, fScore.get(neighborKey));
        }
      }
    }
  }

  return null; // No path found
}

🌐 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",
    priority: "high"
  });

  return { 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: calculateCompletionTime(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.detectStateChanges();
    this.updateWorldState(stateChanges);
  }

  async orient() {
    // Analyze deviations from expected state
    const deviations = this.analyzeDeviations();

    if (deviations.significant) {
      this.triggerReplanning(deviations);
    }
  }

  async decide() {
    if (this.needsReplanning()) {
      await this.replan();
    }
  }

  async act() {
    if (this.currentPlan && this.currentPlan.nextAction) {
      await this.executeAction(this.currentPlan.nextAction);
    }
  }

  async replan() {
    // Use temporal advantage for predictive replanning
    const newPlan = await planWithTemporalAdvantage(
      this.buildCurrentMatrix(),
      this.getCurrentConstraints()
    );

    if (newPlan && newPlan.confidence > 0.8) {
      this.currentPlan = newPlan;

      // Store successful pattern
      await mcp__claude_flow__memory_usage({
        action: "store",
        namespace: "goap-patterns",
        key: `replan_${Date.now()}`,
        value: JSON.stringify({
          trigger: this.lastDeviation,
          solution: newPlan,
          worldState: Array.from(this.worldState.entries())
        })
      });
    }
  }
}

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: {
            epochs: 50,
            learning_rate: 0.001,
            batch_size: 32
          }
        },
        tier: "small"
      });
    } else {
      // Analyze failure patterns
      await this.analyzeFailure(executedPlan, outcome);
    }
  }

  async retrieveSimilarPatterns(currentSituation) {
    // Search for similar successful patterns
    const patterns = await mcp__claude_flow__memory_search({
      pattern: `situation:${this.encodeSituation(currentSituation)}`,
      namespace: "goap-patterns",
      limit: 10
    });

    // Rank by similarity and success rate
    return patterns.results
      .map(p => ({ ...p, similarity: this.calculateSimilarity(currentSituation, p.context) }))
      .sort((a, b) => b.similarity * b.successRate - a.similarity * a.successRate);
  }
}

🎮 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.currentPlan.isValid &&
           !this.worldStateChanged();
  }

  async generatePlan() {
    const startTime = performance.now();

    // Use sublinear solver for rapid planning
    const planMatrix = this.buildPlanningMatrix();
    const constraints = this.extractConstraints();

    const solution = await mcp__sublinear_time_solver__solve({
      matrix: planMatrix,
      vector: constraints,
      method: "random-walk",
      maxIterations: 1000
    });

    const endTime = performance.now();

    this.currentPlan = {
      actions: this.decodeSolution(solution.solution),
      confidence: solution.residual < 1e-6 ? 0.95 : 0.7,
      planningTime: endTime - startTime,
      isValid: true
    };

    return this.currentPlan !== null;
  }
}

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.solution));
    return availableActions[bestActionIndex];
  }

  async calculateUtility(action, currentState, goalState) {
    const timeUtility = await this.estimateTimeUtility(action);
    const costUtility = this.calculateCostUtility(action);
    const riskUtility = await this.assessRiskUtility(action, currentState);
    const goalUtility = this.calculateGoalAlignment(action, currentState, goalState);

    return {
      action,
      timeUtility,
      costUtility,
      riskUtility,
      goalUtility,
      totalUtility: (
        timeUtility * this.utilityWeights.timeEfficiency +
        costUtility * this.utilityWeights.resourceCost +
        riskUtility * this.utilityWeights.riskLevel +
        goalUtility * this.utilityWeights.goalAlignment
      )
    };
  }
}

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.