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.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Expand your Mastra AI agent to use Supabase as a persistent knowledge base. Implement a Mastra `Memory` module that stores and retrieves past analyses and generated recommendations from Supabase. The agent should consult its memory to provide more contextual and personalized responses. For example, if asked about 'memory chip scarcity' again, it should recall its previous analysis and update it with any new data. Provide code snippets for Mastra AI's memory integration and Supabase interactions.
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 Market Trend & Strategy Advisor
Develop an AI-powered strategic advisor agent using the Mastra AI TypeScript framework to monitor and synthesize insights from industry trends, specifically focusing on high-end memory chip supply/demand and AI tool adoption in creative industries. Inspired by headlines regarding data center memory chip scarcity and industry leaders exploring AI tools, this agent will provide nuanced market intelligence and strategic recommendations to a tech executive. The challenge emphasizes building resilient, stateful agents in TypeScript using Mastra AI's built-in memory management and tool integration capabilities. The agent will interact with external APIs to fetch market data, analyze Q&A transcripts, and generate structured reports using Qwen 3. Temporal.io will orchestrate the data fetching and analysis workflows, ensuring reliability and long-running operations, while Supabase will serve as a persistent knowledge base for the agent's memory and collected insights.
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.