Use this when the user wants planning data about plans that should 'make sense' or 'be safe' even if the user did not spell out every rule. Trigger it for requests like 'make itinerary tasks where the plan should feel realistic', 'make sure the agent doesn't leave the stove on', 'create chores where the robot must avoid making the floor slippery', or 'create questions where the agent must avoid repeating the same attraction or inventing fake flights.'
[Case 1]
[Case 2]
[Case 3]
To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:
Phase 1: Environment Exploration
Read the exploration guidelines to discover raw knowledge seeds:
references/EXPLORATION.md
Phase 2: Trajectory Selection
Once Phase 1 is complete, read the selection criteria to evaluate the trajectory:
references/SELECTION.md
Phase 3: Data Synthesis
Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data:
references/SYNTHESIS.md