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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Develop the 'Failure Prediction Agent' that takes input from the Sensor Data Analyst and Maintenance History Agents and uses a predictive model (e.g., a simple threshold-based model or a trained ML model) to determine the likelihood and type of failure. Then, implement the 'Recommendation Agent' which, upon a predicted failure, uses Qwen 2 and the ChromaDB knowledge base to generate detailed, actionable maintenance recommendations. Provide the output for the 'PredictUtilityFailure' evaluation task.
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.
AI-Enhanced Digital Twin for Urban Underground Utility Predictive Maintenance
Cities worldwide face significant challenges managing aging and complex underground infrastructure, as highlighted by the efforts to create an 'All-Utility Underground Map Coming for New York City' and the national push for 'Lead Pipe Removal' (EPA's $4.1B redirection). The integration of 'Advanced Digital Modeling' for projects like the 'Alaskan Wilderness Truss' demonstrates the power of digital twins in construction. This challenge extends that concept to operations and maintenance for urban utilities, where unseen failures can lead to costly disruptions and public safety concerns. This challenge focuses on building an AI-enhanced digital twin system that combines diverse data sources—sensor telemetry, historical maintenance records, and existing GIS/CAD plans—to predict potential failures in urban underground utilities (e.g., water pipes, electrical conduits, gas lines). The system will leverage a multi-agent framework powered by an advanced LLM and a vector database to continuously monitor the infrastructure, identify anomalies, and recommend proactive maintenance strategies, moving beyond reactive repairs.
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.