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.
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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.
Using a provided historical dataset of BESS telemetry (including labeled anomalies), implement TPOT to automatically search for the best performing machine learning pipeline for anomaly detection. Configure TPOT to optimize for F1-score and explore a diverse range of estimators and preprocessing steps. Document the best pipeline found and its performance metrics. Explain how this best pipeline could be deployed into the real-time Ray-based system.
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.
Scalable Anomaly Detection for Distributed BESS Fleets
The proliferation of distributed Battery Energy Storage Systems (BESS), from large utility-scale installations to emerging 'DIY' home batteries, introduces significant operational challenges. Ensuring the safety, reliability, and longevity of these diverse fleets requires sophisticated monitoring and predictive maintenance capabilities. Incidents like 'BESS nightmares' highlight the critical need for early anomaly detection. This challenge tasks participants with designing and implementing a scalable anomaly detection system for a fleet of distributed BESS units. The system must process high-volume, real-time telemetry data (e.g., voltage, current, temperature, SoC) to identify subtle deviations indicative of impending failures, degradation, or unsafe operating conditions. The solution should leverage distributed computing for scalability and automated machine learning techniques for efficient model selection and deployment, with an emphasis on generating actionable insights.
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.