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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using Semantic Kernel's orchestration capabilities, define a 'Plan' or 'Flow' that sequences the actions of your agents: 'Content Ingestor' -> 'Legal Compliance Agent' -> 'Data Formatter'. Illustrate how data (e.g., summaries, compliance reports) is passed between agents and how error handling would be managed within this flow. Provide pseudocode or a high-level Python representation of the Semantic Kernel plan.
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-Powered Content Licensing & Preparation
This challenge focuses on building an advanced multi-agent system to automate the process of identifying, analyzing, and preparing educational content for AI model training. The system will use Claude Opus 4.1 for its superior long-context reasoning to analyze complex licensing agreements and content suitability, alongside OpenAI o3 for high-volume summarization and content generation. LlamaIndex will provide robust data indexing and retrieval capabilities for heterogeneous datasets (e.g., video transcripts, metadata, legal documents). The core innovation lies in implementing the MCP server for secure, verifiable licensing agreement checks and content usage permissions. Agents will collaborate to ingest raw content, extract key information, identify AI training potential, ensure compliance via MCP calls, and then format the data for ingestion by various AI models. This requires sophisticated tool integration with simulated licensing databases and content management 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.