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.
Define the roles, goals, and backstories for a CrewAI team that functions as a 'Thought Partner'. Include roles like 'Human Interface Agent' (powered by Claude Opus 4.1), 'Researcher Agent', 'Analyst Agent', and 'Synthesizer Agent'. Outline their specific responsibilities and how they will collaborate to address complex human queries. Consider how to integrate adaptive thinking budgets into their workflow.
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.
Build a Human-AI Collaborative 'Thought Partner'
This challenge focuses on building an advanced human-AI 'Thought Partner' system. Your goal is to create a Agentkit-based multi-agent team designed to assist humans in complex, open-ended tasks (e.g., strategic planning, creative problem-solving, market analysis) by offering deep insights, generating novel ideas, and synthesizing information. The system will feature adaptive thinking budgets to balance real-time responsiveness with exhaustive research. This 'Thought Partner' will employ a team of specialized agents (e.g., a 'Researcher', an 'Analyst', a 'Synthesizer', and a 'Human Interface Agent') orchestrated by Agentkit. The 'Human Interface Agent' (powered by Claude Opus 4.1) will be the primary point of contact, capable of nuanced understanding of human intent and adaptively allocating tasks to internal expert agents. MCP will be used for tool integration with common enterprise systems and knowledge bases, allowing the agents to 'reach out' for data relevant to the human's query.
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.