القائمة
Sub-skill of dash: 1. Optimize Callback Performance (+3).
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
استنادا إلى تصنيف SOC المهني
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/vamseeachanta/workspace-hub --skill dash-1-optimize-callback-performanceيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
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.
| name | dash-1-optimize-callback-performance |
| description | Sub-skill of dash: 1. Optimize Callback Performance (+3). |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
# Use prevent_initial_call when appropriate
@callback(
Output("output", "children"),
Input("button", "n_clicks"),
prevent_initial_call=True
)
def handle_click(n_clicks):
return f"Clicked {n_clicks} times"
# Use State for non-triggering inputs
@callback(
Output("output", "children"),
Input("submit-btn", "n_clicks"),
State("input-field", "value") # Doesn't trigger callback
)
def submit_form(n_clicks, value):
return f"Submitted: {value}"
# Cache expensive computations
from flask_caching import Cache
cache = Cache(app.server, config={"CACHE_TYPE": "simple"})
@cache.memoize(timeout=300)
def load_data():
return pd.read_parquet("large_file.parquet")
# Separate callbacks into modules
# callbacks/analytics.py
from dash import callback, Output, Input
def register_callbacks(app):
@callback(
Output("chart", "figure"),
Input("dropdown", "value")
)
def update_chart(value):
return create_figure(value)
from dash import callback, Output, Input
from dash.exceptions import PreventUpdate
@callback(
Output("output", "children"),
Input("input", "value")
)
def safe_callback(value):
if value is None:
raise PreventUpdate
try:
result = process(value)
return result
except Exception as e:
return html.Div(f"Error: {str(e)}", className="text-danger")