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.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Define two CrewAI agents: 1) Fabrication_Engineer (Goal: Maximize Ion current density). 2) Metrology_Specialist (Goal: Identify process drift using historical data). Assign both agents to a Letta AI backend so they can store 'experiment logs' in long-term memory. Use the CrewAI 'Process.sequential' flow.
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 role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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.
Digital Twin Process Control for MoS2 FETs using CrewAI and Letta AI
Based on recent breakthroughs in gate structuring for bilayer MoS2 field-effect transistors, this challenge requires you to build an autonomous Digital Twin controller. You will use the CrewAI framework to orchestrate a 'Process Engineer' agent and a 'Metrology Specialist' agent. These agents will collaborate to optimize the gate deposition parameters (temperature, pressure, and time) to achieve ultrahigh current density. To manage the complex, stateful nature of microfabrication cycles, you will integrate Letta AI (formerly MemGPT). Letta AI will provide the agents with 'Archival Memory'—allowing them to store and retrieve historical run data, drift patterns, and sensor telemetry. This prevents the agents from repeating failed experiments and allows them to identify long-term degradation in the virtual manufacturing line.
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.