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.
Develop simple Python functions that simulate 'buy_energy(units, price)' and 'sell_energy(units, price)' on a mock energy exchange. Show how your 'Trader Agent', powered by OpenAI o3, would integrate and use these tools to execute trading decisions based on market analysis from other 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.
OpenAI o3 Smolagents for AI Infrastructure Energy Optimization
Inspired by Meta's strategic move into electricity trading to power its AI infrastructure, this challenge focuses on building an agentic system for real-time energy procurement and consumption optimization. You will deploy a swarm of lightweight Smolagents, powered by OpenAI o3, to act as 'Energy Traders' and 'Infrastructure Managers.' These agents will dynamically interact with a simulated energy market, making informed decisions to minimize costs and ensure reliable power for AI data centers. The system will leverage Haystack for efficient RAG, allowing agents to ingest and analyze real-time energy market data, weather forecasts, and historical consumption patterns. Agents must employ adaptive thinking budgets to react swiftly to market fluctuations and make optimal trading decisions, demonstrating tool integration for interacting with mock energy APIs and internal consumption monitoring systems.
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.