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.
Set up a CrewAI team that leverages your defined Claude Agents SDK agents. Configure the `Crew` with specific roles and tasks. Demonstrate how the 'Chief Strategist' orchestrates the 'Policy Analyst' and 'Market Researcher' to gather information, and then assigns a 'Financial Modeler' (powered by GPT-5 Pro) to synthesize this data and produce preliminary financial impact statements. Detail the workflow using CrewAI's task and process definitions.
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-Driven Geopolitical & Market Strategy with Claude Agents SDK and GPT-5 Pro
This challenge focuses on building a sophisticated multi-agent system designed to assist a strategic consulting firm in evaluating market conditions and policy risks for a tech company's potential IPO. Leveraging the Claude Agents SDK, developers will orchestrate a team of specialized agents, each focused on a specific aspect of the analysis, such as geopolitical risk assessment, market trend analysis, and financial forecasting. The system will integrate advanced AI models and tools to gather, process, and synthesize complex information, demonstrating the power of agentic AI in high-stakes business intelligence. The solution requires agents to perform in-depth research, identify potential market opportunities and regulatory hurdles, and generate actionable strategic recommendations. Emphasis is placed on robust agent communication, structured output generation, and the ability to interact with external tools and simulated data sources. The final output will be a comprehensive strategic report and a dynamic risk assessment dashboard, showcasing the system's capacity for autonomous, data-driven decision support.
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.