goal-plan

name	goal-plan
description	Create and execute Goal-Oriented Action Plans (GOAP) with precondition analysis, cost optimization, and adaptive replanning
argument-hint	<goal-description>
allowed-tools	mcp__claude-flow__task_create mcp__claude-flow__task_list mcp__claude-flow__task_status mcp__claude-flow__task_assign mcp__claude-flow__task_update mcp__claude-flow__task_complete mcp__claude-flow__task_summary mcp__claude-flow__memory_store mcp__claude-flow__memory_search mcp__claude-flow__neural_predict mcp__claude-flow__workflow_create mcp__claude-flow__workflow_execute mcp__claude-flow__workflow_status mcp__claude-flow__hooks_intelligence_trajectory-start mcp__claude-flow__hooks_intelligence_trajectory-step mcp__claude-flow__hooks_intelligence_trajectory-end Bash Read Write Edit

Goal Plan

Create and execute intelligent plans using Goal-Oriented Action Planning (GOAP).

When to use

When you have a complex objective that requires multiple steps, has dependencies between steps, and may need adaptive replanning as conditions change.

Steps

1. Define goal state — what does "done" look like? List concrete success criteria
2. Assess current state — what's true now? What assets, code, infrastructure exist?
3. Identify gap — what must change between current and goal state?
4. Inventory actions — list available actions with:
   • Preconditions (what must be true before this action)
   • Effects (what becomes true after this action)
   • Cost estimate (time, complexity, risk)
5. Generate plan — find the optimal action sequence using A* through the state space
6. Record trajectory — call mcp__claude-flow__hooks_intelligence_trajectory-start to begin tracking
7. Create tasks — call mcp__claude-flow__task_create for each action in the plan
8. Execute — work through tasks in dependency order:
   • Before each action: verify preconditions still hold
   • After each action: verify effects achieved
   • Record each step via mcp__claude-flow__hooks_intelligence_trajectory-step
9. Monitor & replan — if an action fails or produces unexpected results:
   • Reassess current state
   • Recalculate optimal path from new state
   • Update remaining tasks
10. Complete trajectory — call mcp__claude-flow__hooks_intelligence_trajectory-end
11. Store successful plan — call mcp__claude-flow__memory_store with namespace goap-plans

Plan output format

Goal: [concrete objective]
Current State: [key facts]
Plan Cost: [estimated effort]
Steps:
  1. [action] — precondition: [X], effect: [Y], cost: [Z]
  2. [action] — precondition: [Y], effect: [W], cost: [Z]
  ...
Risk Factors: [what could force a replan]
Fallback: [alternative approach if primary path fails]

Replanning triggers

• Action fails (precondition no longer met)
• Unexpected side effects detected
• New information changes goal definition
• Cost exceeds threshold
• External dependency becomes unavailable