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Skill: auto-port custom columns to plugin packages

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难度
4/5
预计耗时
3-5 天
新手友好度
48/100
Issue 类型
功能
描述清晰度
基本清楚
活跃度
冷清
技术栈
python
领域
tooling

调研方向

先查看 docs/plugins/example.md 和 tests_e2e/.../plugins/column_generator/，将其作为脚手架模板，然后检查 .agents/skills/ 的结构。为一个自定义列定义 /port-to-plugin skill，涵盖两种生成策略、参数扁平化以及 models 重写。当生成的 package 可以安装、其 data_designer.plugins entry point 能被发现，并且测试 scaffold 覆盖该转换时，即表示完成。

由索引模型根据 Issue 内容生成。

描述

enhancement

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
Custom column Plugin
@custom_column_generator(required_columns=..., side_effect_columns=...) required_columns / side_effect_columns properties on config class
generator_params: BaseModel fields Promoted to config class fields
model_aliases decorator arg Config field + resource_provider.model_registry access in impl
Function body with row param ColumnGeneratorCellByCell.generate()
Function body with df param ColumnGeneratorFullColumn.generate()
GenerationStrategy choice Base class selection
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
主要语言
Python
星标
2.4k
派生
241
平均合并
1 天 19 分钟
30 天内合并 PR
84

环境准备

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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