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.
Configure the Langroid agent to utilize Claude Sonnet 4 with an adaptive thinking budget for detecting nuanced mental health risks from user chat. Detail how the agent's reasoning budget adjusts based on the severity of detected cues, leading to a decision on whether to escalate or offer support.
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.
Ethical Agent for Adaptive User Safety & MCP Policy
This challenge focuses on building a proactive ethical AI agent system. You will use Langroid to construct a robust, stateful agent capable of monitoring user interactions in real-time, coupled with Smolagents for reactive and lightweight responses. Claude Sonnet 4 will be central to the agent's ability to understand nuanced user sentiment and potential mental health risks. The system must implement age-gated policies and usage limits by dynamically integrating MCP for policy enforcement and leveraging adaptive thinking budgets to determine the appropriate level of intervention or support, including deploying hybrid instant/deep reasoning to balance immediate safety actions with comprehensive ethical analysis. Guidance will be used to ensure structured, safe conversational outputs.
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.