Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Execute the full, refined task plan in a mock robot environment for the task 'Collect and deliver 5 urgent packages to a drop-off zone, avoiding spill zones.' Provide a detailed, step-by-step log of the robot's simulated actions, a comprehensive trace of all agent communications (A2A Protocol), and any dynamic adjustments made by the agent system. Conclude with a final status report on task completion and safety adherence.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the source structure until you know which part of the prompt is actually driving the result quality.
Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.
Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Humanoid Robot Task Planning
With humanoid robotics advancing rapidly, the challenge of autonomous and safe task execution in complex environments becomes paramount. This challenge focuses on designing an advanced multi-agent system to plan and supervise a humanoid robot's actions. Your system will employ CrewAI to orchestrate a team of specialized agents, such as a 'Mission Planner', a 'Safety & Ethics Monitor', and an 'Environmental Sensor Analyst'. Claude Opus 4.1 will power these agents, providing nuanced reasoning capabilities crucial for interpreting complex instructions, handling safety constraints, and adapting to dynamic environments. Agent-to-agent (A2A) Protocol will ensure secure and contextual communication between team members, while Semantic Kernel will be used to integrate and orchestrate a suite of hypothetical robot skills (e.g., navigation, grasping, object identification). The goal is to develop a system that can generate robust, safe, and efficient task plans for a humanoid robot in a simulated operational scenario.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.