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.
Create a Python-based MCP server that implements a tool called 'evaluate_safety'. This tool should take a string and return a safety score between 0 and 1 based on a list of 'blacklisted' prefix patterns.
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.
Frontier AI Safety Monitoring with MCP and Prefix Probing
Following Anthropic's release of their compliance framework for California’s SB53 (Transparency in Frontier AI Act) and research on 'Prefix Probing' (arXiv:2512.16650), this challenge asks you to build an automated safety and transparency dashboard. You will implement a lightweight detection system that monitors LLM inputs and outputs for harmful content using the Prefix Probing technique, which identifies problematic patterns at the start of model generation. Your solution will use the Model Context Protocol (MCP) to integrate a safety-probing server with an active LLM agent. This system must log compliance-related data (like safety triggers and model versioning) as required by SB53, providing a real-world example of how enterprises can maintain 'Social Responsibility Stacks' while using frontier models.
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.