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

# Trait Schema Configuration

> Define custom trait extraction rules

## Schema File Location

Place your trait schema in `config/traits/` and reference it in your configuration:

```json theme={null}
{
  "traits": {
    "schemaPath": "./config/traits/my-schema.traits.json"
  }
}
```

## Schema Format

```json theme={null}
{
  "traits": [
    {
      "key": "trait_name",
      "label": "Human Readable Name",
      "description": "What this trait represents",
      "valueType": "string",
      "category": "category_name",
      "extraction": {
        "enabled": true,
        "promptSnippet": "Extraction hint for the LLM",
        "confidenceThreshold": 0.5
      },
      "injection": {
        "enabled": true,
        "template": "User prefers {{value}}.",
        "priority": 5
      }
    }
  ]
}
```

## Value Types

<Tabs>
  <Tab title="string">
    Free-form text value.

    ```json theme={null}
    {
      "key": "name",
      "valueType": "string"
    }
    ```

    Extracted value: `"Alex"`
  </Tab>

  <Tab title="number">
    Numeric value.

    ```json theme={null}
    {
      "key": "years_experience",
      "valueType": "number"
    }
    ```

    Extracted value: `5`
  </Tab>

  <Tab title="boolean">
    True/false value.

    ```json theme={null}
    {
      "key": "prefers_dark_mode",
      "valueType": "boolean"
    }
    ```

    Extracted value: `true`
  </Tab>

  <Tab title="enum">
    One of predefined values.

    ```json theme={null}
    {
      "key": "expertise_level",
      "valueType": "enum",
      "enumValues": ["beginner", "intermediate", "advanced", "expert"]
    }
    ```

    Extracted value: `"advanced"`
  </Tab>

  <Tab title="array">
    List of values.

    ```json theme={null}
    {
      "key": "interests",
      "valueType": "array"
    }
    ```

    Extracted value: `["Python", "ML", "DevOps"]`
  </Tab>
</Tabs>

## Extraction Settings

### promptSnippet

A hint added to the extraction prompt to help the LLM understand what to look for:

```json theme={null}
{
  "key": "communication_style",
  "extraction": {
    "promptSnippet": "Assess if user prefers formal, casual, technical, or simple communication based on their language and requests"
  }
}
```

### confidenceThreshold

Minimum confidence score required to store the trait:

| Threshold | Use Case                       |
| --------- | ------------------------------ |
| 0.9       | High-stakes traits (name, PII) |
| 0.7       | Important preferences          |
| 0.5       | General traits (default)       |
| 0.3       | Experimental/weak signals      |

## Injection Settings

### template

Format string for injecting the trait into prompts. Use `{{value}}` placeholder:

```json theme={null}
{
  "key": "name",
  "injection": {
    "template": "The user's name is {{value}}. Address them by name when appropriate."
  }
}
```

For array values:

```json theme={null}
{
  "key": "interests",
  "injection": {
    "template": "User is interested in: {{value}}"
  }
}
// Renders as: "User is interested in: Python, ML, DevOps"
```

### priority

Higher priority traits appear earlier in the injected context:

| Priority | Typical Traits         |
| -------- | ---------------------- |
| 10       | Name, language         |
| 8-9      | Communication style    |
| 5-7      | Expertise, preferences |
| 1-4      | Interests, goals       |

## Full Example

```json theme={null}
{
  "traits": [
    {
      "key": "role",
      "label": "Professional Role",
      "description": "User's job title or professional role",
      "valueType": "string",
      "category": "identity",
      "extraction": {
        "enabled": true,
        "promptSnippet": "Extract job title, role, or profession if mentioned",
        "confidenceThreshold": 0.8
      },
      "injection": {
        "enabled": true,
        "template": "User works as a {{value}}.",
        "priority": 8
      }
    },
    {
      "key": "tech_stack",
      "label": "Technology Stack",
      "description": "Technologies and tools the user works with",
      "valueType": "array",
      "category": "context",
      "extraction": {
        "enabled": true,
        "promptSnippet": "Identify programming languages, frameworks, and tools mentioned",
        "confidenceThreshold": 0.6
      },
      "injection": {
        "enabled": true,
        "template": "User's tech stack includes: {{value}}",
        "priority": 6
      }
    }
  ]
}
```