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.
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Structured source with 13 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement the 'Remediation Planning' node within your LangGraph. This node should receive the classified threat type and severity from the 'Threat Analysis' node's state. Use Gemini 2.5 Pro to generate a detailed, actionable remediation plan. Your prompt to Gemini 2.5 Pro should instruct it to output the plan as a list of sequential steps. Include the Python code for this LangGraph node and its interaction with Gemini 2.5 Pro.
```python
from langchain_google_genai import ChatGoogleGenerativeAI
def remediation_planner(state: AgentState) -> AgentState:
threat_type = state["threat_type"]
severity = state["severity"]
llm = ChatGoogleGenerativeAI(model="gemini-2.5-pro", temperature=0.7)
prompt = f"Given a {severity} level {threat_type} threat, generate a sequential, actionable remediation plan. Output as a numbered list of steps.\nThreat: {threat_type}\nSeverity: {severity}\nRemediation Plan:"
response = llm.invoke(prompt)
# ... parse response into a list of strings ...
state["remediation_plan"] = [response.content]
return state
```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.
Cyberthreat Orchestrator Agent
This challenge requires building an autonomous cyber threat detection and remediation system using the LangChain framework, specifically leveraging LangGraph for complex, stateful multi-agent workflows. Developers will design a team of specialized agents that work together to identify threats from simulated log data, analyze their severity, formulate a remediation plan, and orchestrate protective actions. The system must be capable of dynamic decision-making and adapting its response based on the evolving threat landscape. The focus is on robust agent collaboration patterns, sophisticated tool integration, and continuous evaluation of the agent system's effectiveness.
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.