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 28 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.
Create the 'DataAnalyzer' and 'ReportGenerator' nodes. The 'DataAnalyzer' should use Claude Opus 4.1 for complex interpretation of raw data and potentially call the TorchServe tool. The 'ReportGenerator' should use GPT-4o for structuring and summarizing findings, risks, and recommendations into the final report format. Design clear communication protocols between these agents in your LangGraph workflow.
```python
from langchain_anthropic import ChatAnthropic
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import ToolExecutor, ToolNode
# Initialize LLMs
claude = ChatAnthropic(model="claude-3-opus-20240229", temperature=0.2, anthropic_api_key="YOUR_ANTHROPIC_API_KEY")
gpt_4o = ChatOpenAI(model="gpt-4o", temperature=0.2, openai_api_key="YOUR_OPENAI_API_KEY")
# Data Analyzer node (simplified)
def data_analyzer_node(state: AgentState):
print("---DATA ANALYZER---")
raw_data = state["raw_data"]
# Use Claude Opus 4.1 to analyze raw_data and identify patterns/metrics
analysis_prompt = f"Analyze this raw operational data for sustainability metrics: {raw_data}. Identify key consumption figures (water, energy)."
analysis_result = claude.invoke(analysis_prompt).content
# Call TorchServe tool here if needed, e.g., for anomaly_detection
return {"metrics": {"water": 1150.5, "energy": 9500.2}, "messages": [("ai", analysis_result)]}
# Report Generator node (simplified)
def report_generator_node(state: AgentState):
print("---REPORT GENERATOR---")
metrics = state["metrics"]
risks = state["risks"]
recs = state["recommendations"]
# Use GPT-4o to compile a structured report
report_prompt = f"Generate a detailed sustainability report summary based on metrics: {metrics}, identified risks: {risks}, and recommendations: {recs}."
report_summary = gpt_4o.invoke(report_prompt).content
return {"compliance_report": {"report_summary": report_summary}, "messages": [("ai", report_summary)]}
```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.
MCP Server for Enterprise Sustainability Reporting
Develop a cutting-edge agent system designed for enterprise sustainability reporting. This system will leverage the MCP to securely and interpretably access diverse, simulated internal data sources (e.g., IoT sensor readings from factories, energy consumption logs, supply chain data). Agents, orchestrated in a stateful graph, will analyze this data using Claude Opus 4.5 for high-level reasoning and GPT-4o for tool orchestration, integrating a custom anomaly detection model served by TorchServe. The goal is to generate dynamic, accurate sustainability and compliance reports, identify environmental risks, and provide actionable recommendations for optimization.
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.