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.
Implement the 'Sensor Interpreter' and 'Risk Assessor' agents using Langroid with ReAct patterns. The Sensor Interpreter should use MCP tools to access simulated sensor data (e.g., 'get_lidar_readings', 'analyze_camera_feed') and use OpenAI o3 for quick interpretations. The Risk Assessor should receive these interpretations, query Haystack for relevant safety protocols, and use Claude Opus 4.1 for deeper reasoning to identify hazards and potential risks.
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.
Robotic Safety Oversight System with Langroid and Claude Opus 4.1 for Lyte
Inspired by Lyte's focus on enhancing robot perception and safety, this challenge involves building an advanced 'Robotic Safety Oversight' multi-agent system. This system will proactively analyze real-time environmental data and robot state, identify potential hazards, and recommend safe actions to prevent accidents. You will use Langroid for building robust, conversational agents capable of sophisticated tool use, combined with Haystack for contextual grounding in safety protocols. Claude Opus 4.1 will provide deep, ethical reasoning for complex safety scenarios, while OpenAI o3 will be used for rapid, reactive situation assessment within a hybrid reasoning framework. The system will employ ReAct patterns and A2A protocol for seamless communication.
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.