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.
Develop a set of tool functions (e.g., `fetch_web_content`, `perform_web_search`) that your LangGraph nodes can call. These tools should handle robust web scraping and integrate with a chosen search API (e.g., Google Search). Ensure error handling and rate limiting are considered. Integrate these tools into your LangGraph nodes.
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.
Policy-Aware Content Curator
Build an advanced agentic system that can intelligently curate content from the web, similar to how AI browsers handle paywalls or content restrictions. The system must process news articles, summarize them, and apply dynamic content policies (e.g., avoiding sources from a blacklist, prioritizing specific types of reporting, or finding alternative summaries if a paywall is detected). This involves creating a graph-based workflow where different nodes (agents) handle content fetching, policy checking, summarization, and alternative sourcing. The core of this challenge is to leverage Gemini 2.5 Pro's Deep Think mode for complex policy interpretation and nuanced summarization, particularly when adapting to a 'blacklist' or 'preference list' of sources. Implement an adaptive reasoning budget within the LangGraph workflow, allowing the system to spend more computational effort on articles that trigger complex policy decisions or require extensive alternative source discovery.
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.