Support data-driven tables (#57806)

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

data/tables/README.md

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+# Data-driven tables
+
+## Overview
+
+GitHub Docs uses YAML files to manage some complex reference tables instead of hard-to-maintain Markdown tables. This approach provides:
+
+- **Maintainable format**: Stakeholders can easily update readable YAML files
+- **Single source of truth**: Centralized data prevents inconsistencies  
+- **Accurate information**: Reduces errors and outdated content
+- **Self-service process**: Minimal engineering support needed
+
+> **Important**: The `.yml` files in this directory are maintained **manually**. Tables that need automatic updates from external sources require engineering work.
+
+## Table of contents
+
+- [When to use this approach](#when-to-use-this-approach)
+- [How it works](#how-it-works)
+- [Step-by-step guide](#step-by-step-guide)
+- [Testing and validation](#testing-and-validation)
+- [Next steps](#next-steps)
+
+## When to use this approach
+
+Use data-driven tables when you have:
+- Complex reference tables with multiple columns
+- Data that needs regular updates by different stakeholders
+- Structured information that benefits from validation
+
+## How it works
+
+Every data-driven table needs **three files** that work together:
+
+| File type | Location | Purpose |
+|-----------|----------|---------|
+| **Data file** | `data/tables/` | Stores the table content in YAML format |
+| **Content file** | `content/` | Displays the table using Liquid templating |
+| **Schema file** | `src/data-directory/lib/data-schemas/` | Validates the YAML structure |
+
+**Estimated time**: 30-60 minutes for a new table
+
+## Step-by-step guide
+
+### Step 1: Create the data file
+
+Create a new `.yml` file in `data/tables/` with a descriptive name.
+
+**Copilot prompt template:**
+```
+Create a YAML structure that will allow me to generate a table that looks like: 
+[describe your table headers, rows, and columns OR attach an example]
+
+See src/secret-scanning/data/public-docs.yml for an example.
+```
+
+### Step 2: Create the content display
+
+In your content file, add Liquid code to render the table. Access your data at `{% data tables.TABLE_NAME %}` (where `TABLE_NAME` is your filename without `.yml`).
+
+**Copilot prompt template:**
+```
+Create a Markdown table that is dynamically rendered using Liquid code. 
+Pull data from data/tables/TABLE_NAME.yml. 
+The table should look like: [describe your desired output OR attach an example]
+
+See content/code-security/secret-scanning/introduction/supported-secret-scanning-patterns.md for an example.
+Liquid docs: https://shopify.github.io/liquid
+```
+
+**💡 Tip**: Iterate between Steps 1 and 2 until the table renders correctly.
+
+### Step 3: Create the schema file
+
+Create a `.ts` file in `src/data-directory/lib/data-schemas/` with the same name as your YAML file.
+
+**Copilot prompt template:**
+```
+Create a TypeScript schema following prior art under data-schemas that enforces 
+the structure of the data/TABLE_NAME.yml file.
+
+See src/data-directory/lib/data-schemas/learning-tracks.ts for an example.
+```
+
+## Testing and validation
+
+After creating all three files:
+
+1. **Build the site**: Run `npm run build`
+2. **Test schemas**: Run `npm test -- src/data-directory/tests`
+3. **Fix any errors**: If you get failures in `src/data-directory/tests/data-schemas.js`:
+   - Copy the error message
+   - In VS Code Copilot Chat, type: "When I ran the schema test, I got this error:" and paste the error
+   - Update your schema file based on Copilot's suggestions
+4. **Repeat testing** until all tests pass
+
+## Next steps
+
+Once your table is working and tests pass, create a pull request for review.
+
+The `docs-engineering` team must review and approve your implementation.