dash-1-optimize-callback-performance
Sub-skill of dash: 1. Optimize Callback Performance (+3).
Best use case
dash-1-optimize-callback-performance is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of dash: 1. Optimize Callback Performance (+3).
Teams using dash-1-optimize-callback-performance should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/1-optimize-callback-performance/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How dash-1-optimize-callback-performance Compares
| Feature / Agent | dash-1-optimize-callback-performance | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of dash: 1. Optimize Callback Performance (+3).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 1. Optimize Callback Performance (+3)
## 1. Optimize Callback Performance
```python
# 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}"
```
## 2. Efficient Data Loading
```python
# 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")
```
## 3. Modular Callbacks
```python
# 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)
```
## 4. Error Handling
```python
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")
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