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.
Modify your Mastra AI agent to use Claude Opus 4.1 for processing user utterances and generating responses. Configure the agent to send user input to Claude for intent recognition and entity extraction, then use Claude's output to drive tool calls or generate conversational replies. Implement a service that encapsulates Claude API calls and handles error conditions. Show how Claude is integrated into the agent's message processing pipeline.
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.
Build a Hyper-Personalized Voice Assistant Agent
This challenge tasks developers with creating an advanced, real-time voice assistant designed for hyper-personalization and proactive device or application management. The solution must leverage Mastra AI's agentic workflows and robust memory capabilities to maintain long-term user preferences and context. Claude Opus 4.6 will be integrated for its nuanced conversational understanding and empathetic response generation, while ElevenLabs will provide natural, low-latency speech synthesis and recognition for a seamless voice user experience. The assistant should dynamically adapt its responses and actions based on the user's historical interactions and real-time device state. Participants will focus on designing reactive agent workflows in Mastra AI, implementing bidirectional real-time speech interaction, and developing custom tools for device integration. The ultimate goal is to deliver a highly intuitive and personalized voice interface that anticipates user needs and acts intelligently, enhancing the user's interaction with their digital environment.
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.