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.
Create a comprehensive test plan for your agent. Include at least 5 distinct natural language queries covering character names, famous quotes, detailed scene descriptions, and ambiguous requests. For each query, specify the expected behavior, target scene time, and potential failure modes. Outline how you would evaluate the agent's performance in terms of accuracy and robustness.
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.
Agentic Video Scene Skipper
This challenge involves building an advanced agentic system that can interpret complex natural language requests to navigate video content. You will leverage Gemini 3 Pro's multimodal understanding and Langroid's robust agent capabilities to process user queries, perform semantic search over video metadata, and execute simulated playback commands. The system must accurately identify specific scenes based on descriptions, character names, or quotes, demonstrating sophisticated hybrid reasoning and MCP tool integration for real-time control of a simulated media player. This project focuses on combining cutting-edge LLMs with specialized agent frameworks and advanced RAG techniques. You will design a graph-based workflow for parsing queries, retrieving relevant video segments, and interacting with external tools, simulating a highly responsive and intelligent content navigation system. Success will require meticulous prompt engineering, efficient data indexing, and robust error handling to deliver a seamless user experience.
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.