Skill: auto-port custom columns to plugin packages

Problem

Custom columns (@custom_column_generator) are convenient for local/notebook use but hit a hard wall when users need serialization - the generator function serializes to just its __name__, so configs can't round-trip through YAML/JSON. The recommended path is to rewrite as a plugin, but the transformation is mechanical and the boilerplate is easy to get wrong.

Proposal

Add a /port-to-plugin Claude Code skill (.agents/skills/) that takes a Python file containing one or more @custom_column_generator functions and scaffolds a complete, installable plugin package.

Transformation mapping

Output structure

Given /port-to-plugin path/to/my_workflow.py:

data-designer-<name>-plugin/
  src/<package_name>/
    __init__.py
    config.py        # SingleColumnConfig subclass with Literal discriminator
    impl.py          # ColumnGenerator{CellByCell,FullColumn} subclass
    plugin.py        # Plugin() registration object
  pyproject.toml     # Entry point under data_designer.plugins

Scope

• Single custom column per invocation (v1), multi-column bundling as a follow-up
• Rewrites models parameter access to self.resource_provider.model_registry
• Flattens generator_params into config fields
• Uses docs/plugins/example.md and tests_e2e/.../plugins/column_generator/ as scaffolding templates

Why a skill and not a CLI command

The transformation requires understanding intent (naming the discriminator, choosing config field types, handling edge cases in function bodies). An LLM-backed skill can make judgment calls that a rigid code generator can't, while the plugin structure is constrained enough that the output is verifiable.

Acceptance criteria

• Skill file at .agents/skills/port-to-plugin/
• Handles cell-by-cell and full-column strategies
• Handles generator_params flattening
• Handles models parameter rewriting
• Generated plugin installs and passes data_designer.plugins entry point discovery
• Includes a test scaffold

Start by reviewing docs/plugins/example.md and tests_e2e/.../plugins/column_generator/ as scaffolding templates, then inspect the .agents/skills/ structure. Define the /port-to-plugin skill for one custom column, covering both generation strategies, parameter flattening, and models rewriting. Done means the generated package installs, its data_designer.plugins entry point is discovered, and a test scaffold covers the transformation.