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.
Create a simplified 'digital twin' for an SRM component, updating its state based on simulated telemetry and RUL predictions. Use Semantic Kernel to orchestrate a workflow that: 1) ingests telemetry, 2) updates the digital twin's health status, 3) calls the RUL prediction model, and 4) based on predicted RUL and current stock, queries a simulated SerpAPI (or actual, if available) for alternative supplier information.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
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.
SRM Predictive Maintenance & Supply Chain with StarCoder 2 & Semantic Kernel
Develop an AI-driven system for predictive maintenance and optimized supply chain management specifically for Solid Rocket Motors (SRMs) and other critical aerospace components. This challenge requires integrating real-time telemetry data with advanced machine learning models to forecast Remaining Useful Life (RUL) and detect anomalies, thereby preventing costly failures. The system must incorporate a 'digital twin' concept for key components, enabling dynamic health monitoring. Furthermore, it should intelligently optimize the supply chain for spare parts and maintenance scheduling, taking into account factors like lead times, costs, and geopolitical risks. StarCoder 2 will be used to assist in generating and refining data processing or modeling code, while Semantic Kernel will orchestrate the integration of predictive models with external data sources like SerpAPI for real-time market intelligence and supply chain visibility.
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.