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 19 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement the 'Cloud Optimizer Agent' using the Claude Agents SDK. This agent should:
1. Accept user requests for cloud cost optimization for an AI/ML workload.
2. Use Claude Opus 4.1 to analyze a simulated cloud cost report (provide a sample in the prompt context).
3. Propose concrete cost-saving recommendations (e.g., spot instances, reserved instances, S3 lifecycle policies).
4. Include a custom tool that, when invoked, 'sends_optimization_report_via_zapier(report_summary: str)' to simulate triggering an external Zapier workflow for sending an email notification or creating a task. Implement the mock `send_optimization_report_via_zapier` function.
```python
from anthropic.agents import AnthropicAgent, Tool
def send_optimization_report_via_zapier(report_summary: str) -> str:
# Simulate Zapier webhook call or API interaction
print(f"Triggering Zapier with report: {report_summary[:50]}...")
return "Optimization report sent via Zapier."
zapier_tool = Tool(
name="send_optimization_report",
description="Sends an optimization report via a Zapier workflow.",
input_schema={"type": "object", "properties": {"report_summary": {"type": "string"}}, "required": ["report_summary"]},
function=send_optimization_report_via_zapier
)
# Your agent definition will incorporate this tool and logic for analysis.
```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.
AI Patent Analysis & Cloud Optimization Agents
Create an intelligent assistant using Claude Agents SDK that helps navigate the complexities of AI patent law (inspired by the USPTO shift) and simultaneously optimizes cloud resource allocation for AI/ML workloads (addressing cloud backlog). The agent system should be capable of analyzing patent documents, extracting key claims, identifying relevant precedents, and providing recommendations for cloud cost reduction specific to AI infrastructure. The interface will be conversational, leveraging advanced reasoning and tool use.
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.