docs: add initial plugin documentation

docs/code_reference/analysis.md

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+# Analysis
+
+The `analysis` modules provide tools for profiling and analyzing generated datasets. It includes statistics tracking, column profiling, and reporting capabilities.
+
+## Column Statistics
+
+Column statistics are automatically computed for every column after generation. They provide basic metrics specific to the column type. For example, LLM columns track token usage statistics, sampler columns track distribution information, and validation columns track validation success rates.
+
+The classes below are result objects that store the computed statistics for each column type and provide methods for formatting these results for display in reports.
+
+::: data_designer.config.analysis.column_statistics
+
+## Column Profilers
+
+Column profilers are optional analysis tools that provide deeper insights into specific column types. Currently, the only column profiler available is the Judge Score Profiler.
+
+The classes below are result objects that store the computed profiler results and provide methods for formatting these results for display in reports.
+
+::: data_designer.config.analysis.column_profilers
+
+## Dataset Profiler
+
+The [DatasetProfilerResults](#data_designer.config.analysis.dataset_profiler.DatasetProfilerResults) class contains complete profiling results for a generated dataset. It aggregates column-level statistics, metadata, and profiler results, and provides methods to:
+
+- Compute dataset-level metrics (completion percentage, column type summary)
+- Filter statistics by column type
+- Generate formatted analysis reports via the `to_report()` method
+
+Reports can be displayed in the console or exported to HTML/SVG formats.
+
+::: data_designer.config.analysis.dataset_profiler

docs/js/toc-toggle.js

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         // Check if this is a Concepts page (URL contains /concepts/)
         const isConceptsPage = window.location.pathname.includes("/concepts/");
 
-        if (isCodeReferencePage || isConceptsPage) {
-            // Show TOC for Code Reference and Concepts pages by adding class to body
+        // Check if this is a Plugins page (URL contains /plugins/)
+        const isPluginsPage = window.location.pathname.includes("/plugins/");
+
+        if (isCodeReferencePage || isConceptsPage || isPluginsPage) {
+            // Show TOC for Code Reference, Concepts, and Plugins pages by adding class to body
             document.body.classList.add("show-toc");
-            console.log("Code Reference or Concepts page detected - showing TOC");
+            console.log("Code Reference, Concepts, or Plugins page detected - showing TOC");
         } else {
             // Hide TOC for all other pages by removing class from body
             document.body.classList.remove("show-toc");
-            console.log("Non-Code Reference/Concepts page - hiding TOC");
+            console.log("Non-Code Reference/Concepts/Plugins page - hiding TOC");
         }
     });
 } else {

docs/plugins/available.md

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+# 🚧 Coming Soon
+
+This page will list available Data Designer plugins. Stay tuned!

docs/plugins/example.md

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+!!! warning "Experimental Feature"
+    The plugin system is currently **experimental** and under active development. The documentation, examples, and plugin interface are subject to significant changes in future releases. If you encounter any issues, have questions, or have ideas for improvement, please consider starting [a discussion on GitHub](https://github.com/NVIDIA-NeMo/DataDesigner/discussions).
+
+
+# Example Plugin: Index Multiplier
+
+In this guide, we will build a simple plugin that generates values by multiplying the row index by a user-specified multiplier. Admittedly, not the most useful plugin, but it demonstrates the required steps 😜.
+
+A Data Designer plugin is implemented as a Python package with three main components:
+
+1. **Configuration Class**: Defines the parameters users can configure
+2. **Task Class**: Contains the core implementation of the plugin
+3. **Plugin Object**: Connects the config and task classes to make the plugin discoverable
+
+Let's build the `data-designer-index-multiplier` plugin step by step.
+
+## Step 1: Create a Python package
+
+Data Designer plugins are implemented as Python packages. We recommend using a standard structure for your plugin package.
+
+For example, here is the structure of a `data-designer-index-multiplier` plugin:
+
+```
+data-designer-index-multiplier/
+├── pyproject.toml
+└── src/
+    └── data_designer_index_multiplier/
+        ├── __init__.py
+        └── plugin.py
+```
+
+## Step 2: Create the config class
+
+The configuration class defines what parameters users can set when using your plugin. For column generator plugins, it must inherit from [SingleColumnConfig](../code_reference/column_configs.md#data_designer.config.column_configs.SingleColumnConfig) and include a [discriminator field](https://docs.pydantic.dev/latest/concepts/unions/#discriminated-unions).
+
+```python
+from typing import Literal
+from data_designer.config.column_configs import SingleColumnConfig
+
+class IndexMultiplierColumnConfig(SingleColumnConfig):
+    """Configuration for the index multiplier column generator."""
+
+    # Configurable parameter for this plugin
+    multiplier: int = 2
+
+    # Required: discriminator field with a unique Literal type
+    # This value identifies your plugin and becomes its column_type
+    column_type: Literal["index-multiplier"] = "index-multiplier"
+```
+
+**Key points:**
+
+- The `column_type` field must be a `Literal` type with a string default
+- This value uniquely identifies your plugin (use kebab-case)
+- Add any custom parameters your plugin needs (here: `multiplier`)
+- `SingleColumnConfig` is a Pydantic model, so you can leverage all of Pydantic's validation features
+
+## Step 3: Create the task class
+
+The task class implements the actual business logic of the plugin. For column generator plugins, it inherits from [ColumnGenerator](../code_reference/column_generators.md#data_designer.engine.column_generators.generators.base.ColumnGenerator) and must implement a `metadata` static method and `generate` method:
+
+
+```python
+import logging
+import pandas as pd
+
+from data_designer.engine.column_generators.generators.base import (
+    ColumnGenerator,
+    GenerationStrategy,
+    GeneratorMetadata,
+)
+
+# Data Designer uses the standard Python logging module for logging
+logger = logging.getLogger(__name__)
+
+class IndexMultiplierColumnGenerator(ColumnGenerator[IndexMultiplierColumnConfig]):
+    @staticmethod
+    def metadata() -> GeneratorMetadata:
+        """Define metadata about this generator."""
+        return GeneratorMetadata(
+            name="index-multiplier",
+            description="Generates values by multiplying the row index by a user-specified multiplier",
+            generation_strategy=GenerationStrategy.FULL_COLUMN,
+            required_resources=None,
+        )
+
+    def generate(self, data: pd.DataFrame) -> pd.DataFrame:
+        """Generate the column data.
+
+        Args:
+            data: The current DataFrame being built
+
+        Returns:
+            The DataFrame with the new column added
+        """
+        logger.info(
+            f"Generating column {self.config.name} "
+            f"with multiplier {self.config.multiplier}"
+        )
+
+        # Access config via self.config
+        data[self.config.name] = data.index * self.config.multiplier
+
+        return data
+```
+
+**Key points:**
+
+- Generic type `ColumnGenerator[IndexMultiplierColumnConfig]` connects the task to its config
+- `metadata()` describes your generator and its requirements
+- `generation_strategy` can be `FULL_COLUMN`, `CELL_BY_CELL`
+- You have access to the configuration parameters via `self.config`
+- `required_resources` lists any required resources (models, artifact storages, etc.). This parameter will evolve in the near future, so keeping it as `None` is safe for now. That said, if your task will use the model registry, adding `data_designer.engine.resources.ResourceType.MODEL_REGISTRY` will enable automatic model health checking for your column generation task.
+
+!!! info "Understanding generation_strategy"
+    The `generation_strategy` specifies how the column generator will generate data.
+
+    - **`FULL_COLUMN`**: Generates the full column (at the batch level) in a single call to `generate`
+        - `generate` must take as input a `pd.DataFrame` with all previous columns and return a `pd.DataFrame` with the generated column appended
+
+    - **`CELL_BY_CELL`**: Generates one cell at a time
+        - `generate` must take as input a `dict` with key/value pairs for all previous columns and return a `dict` with an additional key/value for the generated cell
+        - Supports concurrent workers via a `max_parallel_requests` parameter on the configuration
+
+## Step 4: Create the plugin object
+
+Create a `Plugin` object that makes the plugin discoverable and connects the task and config classes.
+
+```python
+from data_designer.plugins import Plugin, PluginType
+
+# Plugin instance - this is what gets loaded via entry point
+plugin = Plugin(
+    task_cls=IndexMultiplierColumnGenerator,
+    config_cls=IndexMultiplierColumnConfig,
+    plugin_type=PluginType.COLUMN_GENERATOR,
+    emoji="🔌",
+)
+```
+
+### Complete plugin code
+
+Pulling it all together, here is the complete plugin code for `src/data_designer_index_multiplier/plugin.py`:
+
+```python
+import logging
+from typing import Literal
+
+import pandas as pd
+
+from data_designer.config.column_configs import SingleColumnConfig
+from data_designer.engine.column_generators.generators.base import (
+    ColumnGenerator,
+    GenerationStrategy,
+    GeneratorMetadata,
+)
+from data_designer.plugins import Plugin, PluginType
+
+# Data Designer uses the standard Python logging module for logging
+logger = logging.getLogger(__name__)
+
+
+class IndexMultiplierColumnConfig(SingleColumnConfig):
+    """Configuration for the index multiplier column generator."""
+
+    # Configurable parameter for this plugin
+    multiplier: int = 2
+
+    # Required: discriminator field with a unique Literal type
+    # This value identifies your plugin and becomes its column_type
+    column_type: Literal["index-multiplier"] = "index-multiplier"
+
+
+class IndexMultiplierColumnGenerator(ColumnGenerator[IndexMultiplierColumnConfig]):
+    @staticmethod
+    def metadata() -> GeneratorMetadata:
+        """Define metadata about this generator."""
+        return GeneratorMetadata(
+            name="index-multiplier",
+            description="Generates values by multiplying the row index by a user-specified multiplier",
+            generation_strategy=GenerationStrategy.FULL_COLUMN,
+            required_resources=None,
+        )
+
+    def generate(self, data: pd.DataFrame) -> pd.DataFrame:
+        """Generate the column data.
+
+        Args:
+            data: The current DataFrame being built
+
+        Returns:
+            The DataFrame with the new column added
+        """
+        logger.info(
+            f"Generating column {self.config.name} "
+            f"with multiplier {self.config.multiplier}"
+        )
+
+        # Access config via self.config
+        data[self.config.name] = data.index * self.config.multiplier
+
+        return data
+
+
+# Plugin instance - this is what gets loaded via entry point
+plugin = Plugin(
+    task_cls=IndexMultiplierColumnGenerator,
+    config_cls=IndexMultiplierColumnConfig,
+    plugin_type=PluginType.COLUMN_GENERATOR,
+    emoji="🔌",
+)
+```
+
+## Step 5: Package your plugin
+
+Create a `pyproject.toml` file to define your package and register the entry point:
+
+```toml
+[project]
+name = "data-designer-index-multiplier"
+version = "1.0.0"
+description = "Data Designer index multiplier plugin"
+requires-python = ">=3.10"
+dependencies = [
+    "data-designer",
+]
+
+# Register this plugin via entry points
+[project.entry-points."data_designer.plugins"]
+index-multiplier = "data_designer_index_multiplier.plugin:plugin"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/data_designer_index_multiplier"]
+```
+
+!!! info "Entry Point Registration"
+    Plugins are discovered automatically using [Python entry points](https://packaging.python.org/en/latest/guides/creating-and-discovering-plugins/#using-package-metadata). It is important to register your plugin as an entry point under the `data_designer.plugins` group.
+
+    The entry point format is:
+    ```toml
+    [project.entry-points."data_designer.plugins"]
+    <entry-point-name> = "<module.path>:<plugin-instance-name>"
+    ```
+
+## Step 6: Use your plugin
+
+Install your plugin in editable mode for testing:
+
+```bash
+# From the plugin directory
+uv pip install -e .
+```
+
+Once installed, your plugin works just like built-in column types:
+
+```python
+from data_designer_index_multiplier.plugin import IndexMultiplierColumnConfig
+
+from data_designer.essentials import (
+    CategorySamplerParams,
+    DataDesigner,
+    DataDesignerConfigBuilder,
+    SamplerColumnConfig,
+)
+
+data_designer = DataDesigner()
+builder = DataDesignerConfigBuilder()
+
+# Add a regular column
+builder.add_column(
+    SamplerColumnConfig(
+        name="category",
+        sampler_type="category",
+        params=CategorySamplerParams(values=["A", "B", "C"]),
+    )
+)
+
+# Add your custom plugin column
+builder.add_column(
+    IndexMultiplierColumnConfig(
+        name="v",
+        multiplier=5,
+    )
+)
+
+# Generate data
+results = data_designer.create(builder, num_records=10)
+print(results.load_dataset())
+```
+
+Output:
+```
+  category  multiplied-index
+0        B                 0
+1        A                 5
+2        C                10
+3        A                15
+4        B                20
+...
+```
+
+That's it! You have now created and used your first Data Designer plugin. The last step is to package your plugin and share it with the community 🚀