Quickstart: First Analysis in 5 Minutes

Time commitment: 5 minutes Outcome: Complete a real data analysis with SignalPilot’s AI agent

Step 1: Install SignalPilot

Prerequisites: macOS, Linux, or Windows (WSL) • Internet connection Install SignalPilot:

uvx signalpilot

Don't have uv?

Install uv first (takes seconds):

curl -LsSf https://astral.sh/uv/install.sh | sh

Then run SignalPilot:

uvx signalpilot

See uv installation guide for other methods (Homebrew, Windows, etc.)

What happens:

✓ Creating workspace at ~/SignalPilotHome
✓ Installing isolated Python 3.12 + Jupyter Lab + SignalPilot extension
✓ Installing data packages (pandas, numpy, matplotlib, plotly...)
⚡ Setup complete in ~2 minutes

→ Launching Jupyter Lab at http://localhost:8888

Closed terminal or browser? Relaunch anytime

Option 1: Using uvx (recommended)

uvx signalpilot home

Option 2: Manual activation

cd ~/SignalPilotHome
source .venv/bin/activate && jupyter lab

More installation options

See the Installation Guide for:

Manual installation with pip, conda, or uv
Working in different modes (default, home, project)
Auto-update configuration
Troubleshooting

Step 2: Create Your First Notebook

Create a new notebook

Your browser opened Jupyter Lab at http://localhost:8888You’ll see the SignalPilot panel in the left and right sidebar.Click File → New → Notebook and select Python 3Name it revenue-analysis.ipynb

Always use the default Python 3 kernel. SignalPilot uses this kernel and may not work with other kernels due to missing system variables.

Load your data

Option 1: Use local files (recommended)Type @ in the SignalPilot chat to mention any CSV, Excel, or data file:

Home workspace: ~/SignalPilotHome/data/
Project folder: ./data/ in current working project directory

Use the File Scanner in the left sidebar to add more folders to index.

Option 2: Generate sample dataIf you don’t have data files, create sample data in the first cell:

import pandas as pd
import numpy as np

# Create sample revenue data
dates = pd.date_range('2025-01-01', '2025-12-31', freq='D')
revenue = pd.DataFrame({
    'date': dates,
    'revenue': np.random.randint(1000, 5000, len(dates)) +
               (np.sin(np.arange(len(dates)) / 7) * 500).astype(int)
})

revenue.head()

Expected: DataFrame loaded from file or displayed from generated data

Ask SignalPilot to analyze

Open the SignalPilot chat panel (left sidebar) and ask:

Analyze this revenue data. Show me:
Total revenue for the year
Average daily revenue
A time-series plot showing trends
Identify any anomalies or patterns

Expected: SignalPilot creates multiple cells with:

Aggregation queries
Visualization code
Statistical analysis
Interpretation of results

Iterate on the analysis

Ask a follow-up question:

What's the week-over-week revenue growth rate? Show me a chart.

Expected: SignalPilot adds new cells calculating WoW growth and visualizing it

What You Just Learned

Context Awareness

SignalPilot automatically understood the revenue dataframe in your kernel and used it without you having to copy-paste or describe it.

Multi-Step Planning

The agent broke down your request into multiple steps: aggregation, visualization, and interpretation.

Iteration

You asked a follow-up question, and SignalPilot used context from the previous analysis to build on it.

Code Generation

SignalPilot wrote executable pandas and matplotlib code directly into notebook cells.

Next Steps

Connect Real Data

Connect to your data warehouse

Learn Key Concepts

Understand agents, modes, and context

User Funnel Analysis

Analyze user drop-off with Amplitude and BigQuery

dbt Integration

Connect to your dbt project for lineage

Troubleshooting

SignalPilot doesn't see my dataframe

Solution: Ensure you’ve executed the cell that creates the dataframe. SignalPilot reads from the active kernel state.

Code isn't executing

Solution: Check that you’re in Agent Mode (not Ask Mode). You can switch modes in the SignalPilot panel.

Chart not displaying

Solution: Ensure matplotlib is installed: uv add matplotlib

Introduction

Getting Started

Integrations

Core Concepts

Quickstart: First Analysis in 5 Minutes

Step 1: Install SignalPilot

Step 2: Create Your First Notebook

What You Just Learned

Next Steps

Connect Real Data

Learn Key Concepts

User Funnel Analysis

dbt Integration

Troubleshooting

​Step 1: Install SignalPilot

​Step 2: Create Your First Notebook

​What You Just Learned

​Next Steps

Connect Real Data

Learn Key Concepts

User Funnel Analysis

dbt Integration

​Troubleshooting

Step 1: Install SignalPilot

Step 2: Create Your First Notebook

What You Just Learned

Next Steps

Troubleshooting