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.
Develop Model Context Protocol (Model Context Protocol)-enabled tools to interact with a simulated ad exchange API (e.g., to fetch current bids, available inventory, set prices) and a simulated market data feed (e.g., competitor pricing, seasonal trends). Ensure these tools are accessible by your CrewAI agents.
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.
Dynamic Ad Pricing & Inventory Optimization
This challenge tasks you with building an advanced agentic system for dynamic mobile ad pricing and inventory optimization. Leveraging Claude Opus 4.1, your agents will analyze real-time market trends, predict ad performance, and intelligently adjust pricing and inventory allocation to maximize publisher revenue and fill rates. The core of this system will be a MCP-enabled framework to seamlessly integrate with simulated ad exchanges and external data APIs. This project emphasizes creating a robust, autonomous workflow where specialized agents collaborate to achieve complex business objectives. You'll design agents capable of continuous learning and adaptation, utilizing hybrid reasoning to respond to market fluctuations and identify optimal strategies. The goal is to demonstrate a cutting-edge application of generative AI in a fast-paced commercial environment, pushing the boundaries of automated decision-making.
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.