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.
Design an AutoGen multi-agent team for content curation. Define roles such as 'Content Detector Agent', 'Ethical Policy Agent', and 'Curation Recommender Agent'. Outline their communication flow. Design two MCP-enabled tools: a 'SimulatedAIDetectionTool' (takes content, returns AI likelihood) and a 'PolicyDatabaseTool' (takes theme, returns relevant ethical guidelines).
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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.
Ethical AI Content Curation
Drawing inspiration from video generation models advanced generative capabilities (and the potential for deepfakes), this challenge focuses on building an advanced agentic system for ethical AI-generated content curation. Using AutoGen for dynamic agent orchestration and MCP for tool integration, the system will analyze incoming digital content (simulated text, images, or short video descriptions) to determine its AI origin, identify potential misrepresentation, and curate it based on predefined ethical guidelines. This challenge emphasizes the development of MCP-enabled tools that interact with hypothetical AI detection APIs and ethical policy databases. Agents powered by OpenAI o3 (or GPT-5) will collaborate via A2A protocol to assess content authenticity, potential harm, and suggest appropriate labels or moderation actions, showcasing complex decision-making in a sensitive domain.
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.