Add per-operator sample values and demo workflow templates

### Feature Summary

Step 8 of the operator demo video generator adds the actual demo data: sample values for each operator, workflow templates used during recording, and a playbook for adding new operators.

This is the data consumed by the framework from the previous step. The sample values determine what is entered into each operator's form and therefore whether the generated demo can run successfully and produce meaningful output. The workflow templates provide the upstream data and structure required by different operators, since most cannot be demonstrated on an empty canvas. The playbook provides a repeatable process for adding or updating operator entries so authors do not need to guess the expected format.

### Proposed Solution or Design

This step adds the configuration and supporting files needed to generate operator demo videos

1. Sample values: Add operator-field-values.json, with one entry per operator describing the values used to fill its form during the demo.

For example:
```
"BarChart": {
"fields": "genre",
"categoryColumn": "rating",
"value": "score",
"horizontalOrientation": false
}
```

Entries can also include demo-specific settings, such as which sample dataset the operator uses or which file a source operator should select. The sample dataset schemas are included as well, allowing the validator to confirm that referenced columns actually exist and have compatible types.

2. Workflow templates: Add a small set of sample workflows that operators can be inserted into during recording. Different templates support different operator needs, such as:
- a single-source workflow for most operators,
- a two-input workflow for operators that require multiple inputs,
- a train/test workflow for machine-learning operators,
- datasets with hierarchical or time-series columns for operators that require them.

3. Authoring playbook: Add a short guide for creating or updating an operator's sample values. The author, or an agent assisting the author, can:
- inspect the operator definition to understand its fields and requirements,
- inspect the available sample dataset columns,
- create the sample-value entry,
- run the validator and fix any reported issues.

The playbook also documents common cases that are easy to configure incorrectly, such as fields that expect lists or objects instead of plain strings.

4. Registry placeholder: Add a placeholder for the generated operator registry. The actual registry will be produced by the generator and will not be committed to the repository.

### Affected Area

_No response_

Start by reviewing the demo-generation framework from the previous step and the operator definitions referenced in the proposal. Add operator-field-values.json, sample workflow templates, the authoring playbook, and a placeholder for the generated registry; then run the validator to confirm referenced dataset columns, types, and field shapes are valid.

Written by the indexing model from the issue text.