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.
Implement the A2A Protocol for secure communication between a 'FactCheckerAgent' and a 'SourceVerifierAgent'. The 'FactCheckerAgent' should identify claims needing verification and use A2A to send these claims to the 'SourceVerifierAgent'. The 'SourceVerifierAgent' then queries external RAG sources or simulated APIs, and returns the verification result via A2A, allowing the 'FactCheckerAgent' to update its findings. Demonstrate this collaboration in the `agent_collaboration_log`.
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.
Neutrality Score for Bias Detection & Fact-Checking
Inspired by discussions around content neutrality, this challenge focuses on building an advanced AI agent system capable of analyzing text for bias, factual inaccuracies, and neutrality standards. You will use LangGraph to design a Directed Acyclic Graph (DAG) workflow, orchestrating several specialized agents. Gemini 2.5 Pro (leveraging its Deep Think mode for nuanced analysis) will be central for identifying subtle biases and performing robust factual verification. OpenAI GPT 5 will provide alternative phrasing or counter-arguments to assess different perspectives. The system must implement the A2A (Agent-to-Agent) Protocol for seamless and secure communication between agents during cross-verification processes, ensuring claims are independently assessed. Hybrid instant/deep reasoning will allow agents to quickly triage simple facts while engaging in thorough, multi-step analysis for complex or contentious statements. The output should include a neutrality score and suggested revisions.
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.