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.
Initialize your Claude Agent using the Claude Agents SDK and the `claude-opus-4.1` model. Define the agent's primary role as a news editor. Implement a core function within your agent that takes a news article text as input and generates 3-5 distinct headline options. Focus on headlines that are concise, informative, and neutral in tone. Use Claude's capabilities for summarization and creative text generation. Provide the Python code for agent initialization and the headline generation step.
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 source structure until you know which part of the prompt is actually driving the result quality.
Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.
Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.
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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Agentic News Headline Generator with Fact-Checking and Bias Detection
Following Google's experience with AI-generated headlines sometimes being inaccurate or misleading, this challenge focuses on developing a robust agentic system to generate news headlines. The system, built with the Claude Agents SDK, will emphasize factual accuracy, detect potential biases, and ensure relevance to the source article. Your agent will act as an editorial assistant, using Claude Opus 4.1 for sophisticated reasoning and content generation, combined with external tools for fact-checking and validation. This project highlights the critical role of AI governance, evaluation, and responsible AI practices in content generation workflows.
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.