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.
Design the MCP-enabled tools that your agents will use. Specify at least two external tools (e.g., a simulated financial API, a news aggregator API, a regulatory database) and describe how your agents will interact with them to gather specific data points for market analysis and due diligence.
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.
Hyperscale Data Center Investment
Develop an advanced multi-agent system using CrewAI to analyze the hyperscale data center market. The system should leverage cutting-edge generative AI models like GPT-5 for deep market trend identification, competitive analysis, and strategic investment opportunity generation. Agents will perform extensive research, synthesize complex information from various sources (news, financial reports, industry analyses), and utilize MCP-enabled tools for real-time data integration with enterprise financial systems. This challenge emphasizes building robust, collaborative agent workflows capable of extended thinking to provide actionable insights for investment decisions. Participants will focus on designing clear agent roles, orchestrating their interactions, and integrating external data sources to generate comprehensive market intelligence reports, including due diligence summaries for identified opportunities.
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.