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.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using Llama 3.2 and a vector store, index the key points from the recent NASA Safety Panel report and the SpaceNews article on 'The hidden backbone of space security'. Ensure metadata includes the source and date.
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.
Artemis Safety Analysis & Logistics Optimization using Smolagents and RAGAS
Following the NASA safety panel's recommendation for a comprehensive review of Artemis mission plans, this challenge tasks you with building a 'Safety-First Mission Planner.' Using the Smolagents framework, you will create a tool-calling agent that navigates thousands of pages of historical mission reports (Apollo, Shuttle, ISS) to identify potential safety regressions in the Artemis III/IV flight profiles. You will integrate RAGAS (RAG Assessment Series) to evaluate the quality of the safety recommendations generated by your agent. The system must use Smolagents to perform complex logistics calculations—such as verifying if the Lunar Gateway's fuel reserves can support an emergency return trajectory—while referencing actual NASA safety documentation to justify its reasoning. This combines the precision of symbolic programming (for math) with the contextual depth of LLMs (for safety review).
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.