Declares typed, validated parameters with reactive dependencies for Python classes.
npx skills add https://github.com/MarcSkovMadsen/holoviz-mcp --skill param
Please help me install this Agent Skill. Skill: param Source: https://github.com/MarcSkovMadsen/holoviz-mcp/tree/main/skills/param Command: npx skills add https://github.com/MarcSkovMadsen/holoviz-mcp --skill param
Param provides a concise way to declare typed, validated attributes on Python classes, reducing boilerplate, runtime errors, and wiring of validation logic when building configurable components.
Define a Param-based class with typed attributes and a simple dependency to observe reactive updates.
High-intent search queries and answers about installing and using this skill.
Reactive dependencies in Python parameters enable automatic updates and computed values via the @param.depends decorator. This mechanism tracks parameter changes, triggering computed values and cross-field validation automatically in production code.
The best way to implement configuration objects with cross-field validation is using declarative typed parameters. This method supports dynamic defaults, serialization, and bounds constraints to simplify production-grade configurations without manual validation logic.
You serialize parameter values for reusable Python components by defining declarative typed attributes on classes. This built-in serialization captures the state of validated parameters, allowing configurations and components to be saved and restored reliably.
Yes, declarative parameter typing supports reactive dependencies between Python class attributes. By using the @param.depends decorator, parameter changes automatically trigger updates and recompute dependent values for stateful components.
You should not use declarative typed parameters when your Python data models require no validation, reactive dependencies, or serialization. For simple scripts lacking configurable state or cross-field constraints, standard class attributes are sufficient.
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