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.
Develop Semantic Kernel skills to ingest and process simulated satellite telemetry, parse imagery captions, and perform RAG over ground-based news articles using LlamaIndex. These skills should then feed relevant information into Gemini 2.5 Pro for analysis. Demonstrate the RAG query paths for each data type.
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.
MCP Edge AI for Satellite Data Fusion
Develop an advanced MCP-enabled edge AI system simulating a satellite-based sensor platform. This challenge focuses on fusing real-time sensor data (simulated telemetry and imagery captions) with ground-based contextual information (news articles, geopolitical databases) to identify anomalies or critical events. Participants will design a hybrid reasoning architecture, leveraging Gemini 3 Pro's multimodal capabilities for instant classification and deep analysis, while integrating it with Semantic Kernel for orchestrating complex skills and LlamaIndex for advanced RAG over diverse data sources. The goal is to demonstrate efficient and secure data synthesis at the edge, communicating results to a ground station via a robust MCP. This project highlights the complexities of operating LLMs in constrained environments and the power of intelligent agents for critical infrastructure monitoring.
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.