Quickstart: Kumo Coding Agent

This guide helps you get started with the Kumo Coding Agent for KumoRFM Pre-Trained workflows.

Use this page when you already have a project, notebook, or editor environment available and want a short path to productive agent-assisted work.

What this quick start covers:

• choosing an environment for agent-assisted work
• connecting the Kumo Coding Agent to your local project or notebook flow
• using Codex or Claude Code for setup help, graph definition, and predictive query authoring
• verifying that the agent can work with your KumoSDK project context

Choose your environment:

Try These Prompts

All examples use a sample e-commerce dataset: s3://kumo-sdk-public/rfm-datasets/online-shopping

Copy and paste these directly into your coding agent (Claude Code, Codex, or Cursor).

Explore the Data

Start here. The agent will load the dataset, inspect every table, and summarize what it finds.

Load the data from s3://kumo-sdk-public/rfm-datasets/online-shopping and
tell me what tables are available, how many rows each has, and what the
columns and types look like.

Build a Graph

Once you understand the data, ask the agent to build a relational graph.

Build a KumoRFM graph from the online-shopping dataset at
s3://kumo-sdk-public/rfm-datasets/online-shopping. Inspect the schema,
infer relationships between tables, validate the graph, and visualize it.

Write a Prediction Query

Ask the agent to write a PQL query for a specific business question.

Using the online-shopping graph, write a PQL query that predicts whether
each customer will make a purchase in the next 14 days.

Run an End-to-End Prediction

This is the full workflow in one prompt. The agent will load data, build the graph, write PQL, run the prediction, and show results.

Run a complete KumoRFM prediction on the online-shopping dataset at
s3://kumo-sdk-public/rfm-datasets/online-shopping. Predict the total
spend per customer over the next 30 days. Show me the results and
evaluate the prediction quality.

Explain the Results

After running a prediction, ask the agent to explain what drove the results.

Pick one customer from the predictions and explain what features were
most important in driving their predicted spend.

Try a Different Question

Change the prediction task to explore what else the data can answer.

Using the same online-shopping graph, predict which product category
each customer is most likely to buy from next.

What to Expect

When you run these prompts, the agent will:

1. Inspect the data first before writing any code
2. Build the graph with correct table names and relationships
3. Write PQL using real column names from the schema
4. Run the prediction and show sample output
5. Evaluate the results with appropriate metrics

If something goes wrong, use the agent to debug and iterate until the prediction runs successfully:

The prediction failed. Can you look at the error, fix the issue, and
re-run it?

The evaluation metrics look weak. Can you try a different time window
or aggregation and compare the results?

Can you try a different prediction target on this dataset and see if
we get better results?

The goal is to keep iterating with the agent until you have a working, high-quality prediction notebook or script.

Next Steps

• Kumo Coding Agent on GitHub for agent source, context files, and skills
• Setup for SDK fundamentals
• Make Predictions for PQL query reference
• Prediction Types for supported prediction types
• Environment Setup for detailed editor and notebook setup