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 a testing module that simulates various input scenarios (authentic, subtle counterfeit, obvious counterfeit). Implement the final reporting mechanism that generates a detailed, human-readable authenticity report, including a confidence score, evidence, and any detected issues, referencing the trace data obtained via MCP tools.
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 rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.
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.
MCP-Enabled AI for Luxury Authenticity on TikTok Shop
The luxury retail market is expanding rapidly on platforms like TikTok Shop, but with this growth comes the critical challenge of authenticating high-value items, many sold by secondhand resellers. This challenge focuses on building a cutting-edge multi-modal generative AI system that leverages advanced agent frameworks to verify the authenticity of luxury goods. Developers will integrate real-time image analysis, historical data lookup, and supply chain provenance to ensure every item meets stringent authenticity standards. Participants will design a sophisticated LangGraph-based workflow, orchestrating multiple specialized agents to perform detailed checks, cross-reference databases, and identify anomalies. The system will utilize the multi-modal capabilities of Gemini 3 to process visual cues, textual descriptions, and potentially even audio or video input for a comprehensive assessment. Crucially, the solution will incorporate the MCP for seamless tool integration with external enterprise systems and blockchain explorers, ensuring agents can access and process vast amounts of structured and unstructured data for definitive authenticity decisions. This challenge pushes the boundaries of AI in e-commerce, creating a verifiable and trustworthy luxury marketplace.
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.