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rdst-python-toolkit

Automate RDST Python CLI, API, and UI toolkit development.

Updated Apr 28, 2026
Search Tags:#ui#api#python#cli#toolkit#llm-prompt#rdst
One-click install
npx skills add https://github.com/readysettech/rdst --skill rdst-python-toolkit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rdst-python-toolkit
Source: https://github.com/readysettech/rdst/tree/main/.claude/skills/rdst-python-toolkit
Command: npx skills add https://github.com/readysettech/rdst --skill rdst-python-toolkit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the development and extension of the RDST Python CLI, API, and UI toolkit.

Core Features & Use Cases

  • Provides a structured framework for adding CLI commands, FastAPI routes, Rich UI components, and consistent LLM prompt workflows.
  • Supports modular organization in lib/ (ui, services, functions, llm_manager) to enable end-to-end tooling across development, testing, and deployment scenarios.
  • Use Case: When integrating a new database diagnostic flow, extend the toolkit to expose a new CLI command, API endpoint, and UI widget with shared validation logic.

Quick Start

Run the toolkit scaffolding by wiring a new CLI command into rdst.py and importing the corresponding modules from lib/ to expose the feature.

Frequently Asked Questions about rdst-python-toolkit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add a new CLI command and API endpoint to a Python toolkit?▼

To add a new CLI command and API endpoint, wire the command into rdst.py and import the corresponding modules from lib/ to expose the feature. This scaffolding approach shares validation logic across the CLI, API, and UI layers.

What is the best way to structure a Python project with CLI, API, and UI components?▼

Structuring a Python project with modular organization in lib/ separates UI, services, functions, and llm_manager components. This enables end-to-end tooling across development, testing, and deployment scenarios for consistent integration.

How do I create consistent LLM prompt workflows in a Python application?▼

Creating consistent LLM prompt workflows uses deterministic prompting integrated within the lib.llm_manager module. This supports evolving LLM workflows alongside CLI commands and API endpoints for automated development.

Can I share validation logic between a FastAPI route and a Rich UI component?▼

You can share validation logic between FastAPI routes and Rich UI components by extending the toolkit with modular code organization. Integrating a new flow exposes a CLI command, API endpoint, and UI widget with shared validation.

Does this Python CLI toolkit require external dependencies to build UI components?▼

This Python CLI toolkit requires no external dependencies to build UI components. It internally supports creating Rich UI components and FastAPI routes using modular integration patterns from the lib/ directory structure.

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