Define requirements for an OpenClaw integration
Document a proposed integration's data needs, actions, credentials, and review boundaries.
Create and execute Goal-Oriented Action Plans (GOAP) with precondition analysis, cost optimization, and adaptive replanning
--- name: goal-plan-fcb32cec16f9 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--- name: goal-plan-fcb32cec16f9 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
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.