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 25 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using the Claude Agents SDK, build the 'Compliance Policy Agent'. This agent should receive transcribed content (from the 'Content Ingestor/Analyzer Agent') and a list of policy rules. The agent must use Claude Opus 4.1's advanced reasoning to determine if the content violates any rules, identify the specific violation type, and provide a clear explanation. Configure the agent to be verbose, demonstrating its thought process in making a decision. Show how you define the agent and its interaction loop.
```python
from anthropic import Anthropic
from claude_agents.api import Agent, AgentBuilder
# Initialize Anthropic client
client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
def check_compliance(content: str, policy_rules: list[str]) -> dict:
# Agent logic will go here. Example prompt for Claude.
prompt = f"Given the content: '{content}' and these policy rules: {policy_rules}. " \
"Determine if any rule is violated. If so, identify the violation type, explain why, and suggest an action."
response = client.messages.create(
model="claude-3-opus-20240229", # Use Claude Opus 4.1
max_tokens=1000,
messages=[
{"role": "user", "content": prompt}
]
)
# Parse response to structured dict
return {"decision": "violating", "violation_type": "...", "explanation": "...", "recommended_action": "..."}
# Example of how you'd define this as an agent skill
# compliance_agent_skill = AgentBuilder(client=client).tool(
# name="check_content_compliance",
# description="Checks content against a set of policy rules."
# )(check_compliance)
```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.
Real-Time AI Content Compliance Monitor
This challenge focuses on developing a real-time AI content compliance monitoring system using the Claude Agents SDK, inspired by tightening regulations on AI-generated and manipulated social media content. Participants will build an autonomous agent capable of analyzing incoming content streams (simulated audio, text, and potentially visual metadata) to detect policy violations related to misinformation, AI-generated fakes, or sensitive material. The system must rapidly identify issues and trigger appropriate compliance actions within strict timeframes. The core of the challenge involves designing agents with advanced reasoning capabilities, robust tool-calling for content analysis (e.g., audio transcription, text classification), and the ability to interpret complex regulatory guidelines. The solution should demonstrate Claude's extended thinking for nuanced policy interpretation and autonomous decision-making in a high-stakes, real-time environment.
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.