用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/a5c-ai/babysitter --skill textual-scaffolder命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
| name | textual-scaffolder |
| description | Generate Textual (Python) TUI application structure with widgets, screens, and CSS styling. |
| allowed-tools | Read, Write, Edit, Bash, Glob, Grep |
| graph | {"domains":["domain:software-engineering"],"specializations":["specialization:cli-mcp-development"],"skillAreas":["skill-area:cli-design","skill-area:command-line-interface-tools"],"roles":["role:backend-engineer","role:platform-engineer"],"workflows":["workflow:feature-development"],"topics":["topic:developer-experience"]} |
Generate Textual TUI applications with Python and modern async patterns.
Invoke this skill when you need to:
| Parameter | Type | Required | Description |
|---|---|---|---|
| projectName | string | Yes | Project name |
| screens | array | No | Screen definitions |
| widgets | array | No | Custom widget definitions |
from textual.app import App, ComposeResult
from textual.widgets import Header, Footer, Static, Button, Input
from textual.containers import Container, Horizontal, Vertical
from textual.screen import Screen
class MainScreen(Screen):
"""Main application screen."""
CSS = """
MainScreen {
layout: grid;
grid-size: 2;
grid-gutter: 1;
}
#sidebar {
width: 30;
background: $surface;
border: solid $primary;
}
#content {
background: $surface;
border: solid $secondary;
}
"""
def compose(self) -> ComposeResult:
yield Header()
yield Container(
Static("Sidebar", id="sidebar"),
Static("Content", id="content"),
)
yield Footer()
class MyApp(App):
"""Main TUI application."""
BINDINGS = [
("q", "quit", "Quit"),
("d", "toggle_dark", "Toggle dark mode"),
]
CSS_PATH = "styles.tcss"
def on_mount(self) -> None:
self.push_screen(MainScreen())
def action_toggle_dark() -> :
.dark = .dark
__name__ == :
app = MyApp()
app.run()
from textual.widget import Widget
from textual.reactive import reactive
from textual.message import Message
class Counter(Widget):
"""A counter widget with increment/decrement."""
value = reactive(0)
class Changed(Message):
"""Counter value changed."""
def __init__(self, value: int) -> None:
self.value = value
super().__init__()
def render(self) -> str:
return f"Count: {self.value}"
def increment(self) -> None:
self.value += 1
self.post_message(self.Changed(self.value))
def decrement(self) -> None:
self.value -= 1
self.post_message(self.Changed(self.value))
Screen {
background: $surface;
}
Header {
dock: top;
background: $primary;
}
Footer {
dock: bottom;
background: $primary;
}
Button {
margin: 1;
}
Button:hover {
background: $primary-lighten-1;
}
Input {
margin: 1;
border: tall $secondary;
}
Input:focus {
border: tall $primary;
}
.error {
color: $error;
}
.success {
color: $success;
}
from textual.widgets import DataTable
from textual.app import ComposeResult
class DataScreen(Screen):
def compose(self) -> ComposeResult:
yield DataTable()
def on_mount(self) -> None:
table = self.query_one(DataTable)
table.add_columns("Name", "Email", "Role")
table.add_rows([
("Alice", "alice@example.com", "Admin"),
("Bob", "bob@example.com", "User"),
("Charlie", "charlie@example.com", "User"),
])
[project]
dependencies = [
"textual>=0.40.0",
]
[project.optional-dependencies]
dev = [
"textual-dev>=1.0.0",
]
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
基于 SOC 职业分类