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 18 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Extend your agent's workflow. After GPT-4o generates an initial compliance advice using 'query_legal_database', introduce a step where a separate call to Claude Opus 4.1 is made. Claude's role is to critically review GPT-4o's advice for potential biases or inaccuracies, especially regarding complex legal interpretations. Describe the prompt you would use for Claude Opus 4.1 and how you would integrate this verification step programmatically using the OpenAI Agents SDK's conversational flow.
```python
import anthropic
# client = anthropic.Anthropic(api_key="YOUR_CLAUDE_API_KEY")
def verify_advice_with_claude(gpt4o_advice: str, query: str) -> str:
prompt = f"Critically review the following legal advice provided by another AI for potential inaccuracies, biases, or omissions. Focus on clarity and legal correctness.\nOriginal query: {query}\nAdvice to review: {gpt4o_advice}\nYour assessment:"
# response = client.messages.create(
# model="claude-3-opus-20240229", # Or Opus 4.1 if available
# max_tokens=500,
# messages=[
# {"role": "user", "content": prompt}
# ]
# )
# return response.content[0].text
return "Claude's verified assessment."
# Integrating this into the OpenAI Assistant's thread logic would involve a custom callback
# or explicit function call after GPT-4o's initial response, managing the multi-turn.
```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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Global Tax & Legal Compliance Advisor Agent
This challenge focuses on developing a sophisticated legal and tax compliance advisor using the OpenAI Agents SDK. The agent will interpret complex regulatory texts, answer specific compliance queries for various jurisdictions, and justify its advice by citing relevant statutes. A core component will be the integration with a simulated MCP knowledge base, powered by Pinecone, to provide the agent with a vast, searchable repository of legal and tax documents. The challenge emphasizes advanced tool use, multi-LLM verification (using GPT-4o for primary analysis and Claude Opus 4.1 for cross-validation), and rigorous evaluation of accuracy and transparency.
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.