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Sub-skill of dash: 5. Multi-Page Applications.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/vamseeachanta/workspace-hub --skill dash-5-multi-page-applications명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
SKILL.md 표시 중
SOC 직업 분류 기준
| name | dash-5-multi-page-applications |
| description | Sub-skill of dash: 5. Multi-Page Applications. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Project Structure:
my_dash_app/
├── app.py # Main entry point
├── pages/
│ ├── __init__.py
│ ├── home.py
│ ├── analytics.py
│ └── settings.py
├── components/
│ ├── __init__.py
│ ├── navbar.py
│ └── footer.py
├── utils/
│ ├── __init__.py
│ └── data.py
└── assets/
├── style.css
└── logo.png
Main App (app.py):
from dash import Dash, html, dcc, page_container
import dash_bootstrap_components as dbc
app = Dash(
__name__,
use_pages=True,
external_stylesheets=[dbc.themes.BOOTSTRAP]
)
# Navbar
navbar = dbc.NavbarSimple(
children=[
dbc.NavItem(dbc.NavLink("Home", href="/")),
dbc.NavItem(dbc.NavLink("Analytics", href="/analytics")),
dbc.NavItem(dbc.NavLink("Settings", href="/settings")),
],
brand="My Dashboard",
brand_href="/",
color="primary",
dark=True,
)
# Layout with navigation and page container
app.layout = html.Div([
navbar,
dbc.Container([
page_container
], fluid=True, className="mt-4")
])
if __name__ == "__main__":
app.run(debug=True)
Home Page (pages/home.py):
from dash import html, register_page
import dash_bootstrap_components as dbc
register_page(__name__, path="/", name="Home")
layout = dbc.Container([
dbc.Row([
dbc.Col([
html.H1("Welcome to the Dashboard"),
html.P("Select a page from the navigation bar to get started."),
dbc.Card([
dbc.CardBody([
html.H4("Quick Links"),
dbc.ListGroup([
dbc.ListGroupItem("Analytics", href="/analytics"),
dbc.ListGroupItem("Settings", href="/settings")
])
])
])
])
])
])
Analytics Page (pages/analytics.py):
from dash import html, dcc, callback, Output, Input, register_page
import dash_bootstrap_components as dbc
import plotly.express as px
import pandas as pd
register_page(__name__, path="/analytics", name="Analytics")
# Generate sample data
df = pd.DataFrame({
"date": pd.date_range("2025-01-01", periods=365),
"value": [100 + i + (i % 30) * 5 for i in range(365)]
})
layout = dbc.Container([
html.H1("Analytics"),
dbc.Row([
dbc.Col([
dbc.Label("Chart Type"),
dcc.Dropdown(
id="chart-type",
options=[
{"label": "Line", "value": "line"},
{"label": "Bar", "value": "bar"},
{"label": "Area", "value": "area"}
],
value="line"
)
], md=4)
], className="mb-4"),
dcc.Graph(id="analytics-chart")
])
@callback(
Output("analytics-chart", "figure"),
Input()
)
():
chart_type == :
fig = px.line(df, x=, y=)
chart_type == :
monthly = df.resample(, on=)[].().reset_index()
fig = px.bar(monthly, x=, y=)
:
fig = px.area(df, x=, y=)
fig