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.
Integrate the trained anomaly detection model with a Langroid agent. When an anomaly is detected, the agent should query the model's output and then use the Anthropic API to generate a detailed, context-aware explanation of the anomaly, including potential causes and recommended follow-up actions. Focus on crafting effective prompts for the Anthropic API to achieve high-quality, actionable explanations.
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-Powered SSA: Anomaly Detection with Gemma and Explainable AI
The increasing congestion of orbital space necessitates advanced Space Situational Awareness (SSA) capabilities to identify anomalous object behavior, predict potential collisions, obtain insights into mission profiles, and assess potential threats. This challenge focuses on developing an intelligent system that uses synthetic or real-world space object telemetry to detect and categorize anomalous events. Participants will design, implement, and evaluate a machine learning pipeline capable of identifying unusual orbital maneuvers, conjunction events, or non-standard satellite operations. The system should go beyond simple detection by leveraging large language models to provide actionable, explainable insights into the nature and potential implications of detected anomalies, crucial for both civil and defense space tracking efforts. The solution will integrate a robust anomaly detection model, potentially built with or informed by Gemma 2 principles, processing diverse sensor data (e.g., TLEs, radar, optical observations). Furthermore, it will utilize the Langroid framework to build an interactive agent that queries the anomaly detection system and uses the Anthropic API to generate rich, context-aware explanations for identified anomalies. This will enable human operators to quickly understand complex situations and make informed decisions, directly supporting enhanced Space Situational Awareness and threat assessment capabilities.
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.