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.
Design the LangGraph workflow: User input goes to `InteractionAgent`, then to `BehaviorAnalyst`, whose output informs the `FluencyCoach`. The `FluencyCoach`'s feedback should then be presented to the user, and their response fed back into the `InteractionAgent` to form a continuous evaluation and coaching loop. Include conditional edges for adaptive branching based on user feedback or identified behaviors.
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 Fluency Index Evaluator with LangGraph and OpenAI o4-mini
Anthropic's AI Fluency Index highlights key behaviors for effective human-AI collaboration. This challenge involves building a multi-agent system using LangChain with LangGraph to act as an "AI Fluency Coach." The system will interact with users, observe their collaboration patterns (simulated), evaluate these against the Fluency Index behaviors, and provide actionable feedback. It will utilize specialist agents powered by OpenAI o4-mini and Llama 4 Maverick for understanding user input, analyzing behavior, and generating coaching advice. The objective is to demonstrate how graph-based agent orchestration can create dynamic, adaptive evaluation and improvement systems for human-AI interaction.
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.