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Prompt to Prepare a Shopping Cart from a Recipe Photo

The author reports that Grok Bot receives a recipe photo, organizes the ingredients and quantities, and creates a shopping list by excluding ingredients already available at home.

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Agent Investigates Detailed Data and Deployment Records When Metrics Change Rapidly

This is an example where, when an anomaly is detected in a metric, the agent selects and investigates detailed figures, related metrics, and recent deployments.

n8n

How it works

  1. Prepare tools to query metric definitions, history, detailed groups, and recent deployment records.
  2. When an anomaly is detected, the agent selects tools for metric definitions, history, segments, related metrics, and recent deployments to gather evidence for the cause.

Results

In the public example, the intended outcome is a list of possible causes for the metric change and the evidence collected during the investigation.

Notes

The determination of anomalies is handled by statistical code, while the agent is responsible for the subsequent investigation of causes. The agent does not confirm causality based solely on the simultaneous occurrence of deployments and metric changes. The author reports that public settings and tool connections were checked, but the process was not executed directly.

Source

n8n 공개 워크플로 #19804 Original post ↗

Example prompt · n8n

Goal: When a metric changes rapidly, investigate both detailed data and recent deployments.

Input & Environment: [Metric history], [Anomaly detection criteria], [Detailed groups], [Recent deployment records].

Reference original: https://n8n.io/workflows/19804

Prepare the settings and connections so that a test environment can be created using the public configuration from the original source. If there are missing sub-workflows, APIs, or account permissions, mark them as incomplete until they are actually connected.

Connections and data to prepare: Prepare tools to query metric definitions, history, detailed groups, and recent deployment records.

Agent actions to implement: When an anomaly is detected, the agent selects tools for metric definitions, history, segments, related metrics, and recent deployments to gather evidence for the cause.

Scope of actions: Anomaly determination is handled by statistical code, and the agent is responsible for the subsequent investigation of causes. The agent does not confirm causality based solely on the simultaneous occurrence of deployments and metric changes.

The deliverable is a list of possible causes for the metric change and the evidence collected. Please also organize the necessary setup steps and actual execution methods. After checking the results returned by the tools, select the next task, and separately note any data that could not be accessed or tasks that could not be executed.

Verification: Distinguish between missing data and actual rapid changes, and ensure that data with only correlation is not mistaken for causation. Show the inputs used for verification, the actual outputs, and clearly separate any items that have not yet been confirmed.