data-analysis-caching-for-performance
Sub-skill of data-analysis: Caching for Performance (+2).
Best use case
data-analysis-caching-for-performance is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of data-analysis: Caching for Performance (+2).
Teams using data-analysis-caching-for-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/caching-for-performance/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-analysis-caching-for-performance Compares
| Feature / Agent | data-analysis-caching-for-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 data-analysis: Caching for Performance (+2).
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
# Caching for Performance (+2)
## Caching for Performance
```python
import streamlit as st
from functools import lru_cache
@st.cache_data(ttl=3600) # Streamlit caching
def load_and_process_data():
return pl.read_parquet("data.parquet")
@lru_cache(maxsize=100) # General Python caching
def expensive_calculation(params_tuple):
return compute_metrics(params_tuple)
```
## Consistent Styling
```python
# Define color palette
COLORS = {
"primary": "#1f77b4",
"secondary": "#ff7f0e",
"success": "#2ca02c",
"danger": "#d62728",
"neutral": "#7f7f7f"
}
*See sub-skills for full details.*
## Error Handling for Data Loading
```python
def safe_load_data(path, fallback=None):
"""Load data with comprehensive error handling."""
try:
if path.endswith('.parquet'):
return pl.read_parquet(path)
elif path.endswith('.csv'):
return pl.read_csv(path)
else:
raise ValueError(f"Unsupported format: {path}")
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