> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sticklight.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Get up and running with Sticklight.

## Getting Started

Welcome! Follow the instructions below to learn how to capture data, get structured insights, and answer business questions with Sticklight.

// Fancy platform screenshot here

<Steps>
  <Step title="Generate a Sticklight API key">
    1. [Login](https://platform.sticklight.io/login) to the Sticklight platform.

    2. Navigate to [API Keys settings](https://platform.sticklight.io/settings/api-keys) and click "Generate New Key". Give the key a descriptive name, and store it securely.
  </Step>

  <Step title="Use our SDKs to capture runtime data">
    Choose the SDK that matches your tech stack:

    <AccordionGroup>
      <Accordion title="Python SDK" icon="python">
        Install and use our Python SDK to capture events from Python applications. See the [Python SDK guide](/integrations/python-sdk).
      </Accordion>

      <Accordion title="TypeScript SDK" icon="js">
        Install and use our TypeScript SDK to capture events from JavaScript/TypeScript applications. See the [TypeScript SDK guide](/integrations/typescript-sdk).
      </Accordion>
    </AccordionGroup>
  </Step>

  <Step title="Interact with your data in Views">
    [Views](/learn/views/views) are vantage points over your captured data. They are specialized tables with exploration tools for resolving key product questions.

    1. In the [Sticklight Platform](https://platform.sticklight.io), navigate to Views

    2. Extract structured insights from your data by creating Properties
  </Step>

  <Step title="Create your first Property">
    Properties enrich your data for deeper understanding. Sticklight offers two types of Properties:

    <AccordionGroup>
      <Accordion title="Calculated Properties" icon="function">
        [Calculated Properties](/learn/properties/properties) process and aggregate any aspect of your data, including other Properties you have created.

        1. From your View, click "New Column" and select "Calculated"

        2. Name your property (e.g., "first\_conversation\_message")

        3. Select the Source Column you wish to analyze (e.g., "message\_content")

        4. Select an Aggregation Function to apply to the Source Column (e.g., "First Value")

        5. Optionally, define a filter to focus the calculation on a part of your data.&#x20;

           In our example, we'll filter to only include messages in conversations that have more than 4 messages.

        6. Click "Create" to save your Property

        A new "first\_conversation\_message" column will be added to your View. It will be available for use throughout the platform.
      </Accordion>

      <Accordion title="Magic Properties" icon="sparkles">
        [Magic Properties](/learn/properties/properties) leverage natural language and LLMs to create insightful new Properties.

        1. From your View, click "New Column" and select "Magic"

        2. Describe what you want to discover using natural language with column references:

           ```
           What is the sentiment of @first_conversation_message in a scale of 1-5?
           ```

        3. Choose the output type ("Number", in this case)

        4. Name your property (e.g., "first\_message\_sentiment") and click "Save"

        <Frame>
          <img src="https://mintlify.s3.us-west-1.amazonaws.com/sticklight/images/magic-prop-creation-cropped.png" />
        </Frame>

        Magic Properties are powerful for extracting subjective, complex insights without having to think in SQL.
      </Accordion>
    </AccordionGroup>
  </Step>

  <Step title="Create your first Chart">
    // The text below is LLM generated

    [Charts](/learn/charts) transform your View data into visual representations that reveal patterns and trends:

    1. Navigate to the Charts section and click "Create Chart"

    2. Select the View you want to visualize

    3. Choose a chart type (line, bar, etc.) based on what you want to analyze

    4. Configure the axes and series:

       * X-axis: Time or a categorical property

       * Y-axis: A numeric property or count

       * Series: Optional grouping by a categorical property

    5. Add filters to focus on specific data segments if needed

    6. Name your chart and click "Create"

    For example, you might create a line chart showing "Average Response Sentiment" over time, grouped by "Model Type" to compare different LLMs.

    Charts help you identify trends that might not be obvious from tabular data and make it easier to communicate insights with teammates.
  </Step>
</Steps>