data-analysis-1-lazy-evaluation-first
Sub-skill of data-analysis: 1. Lazy Evaluation First (+3).
Best use case
data-analysis-1-lazy-evaluation-first is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of data-analysis: 1. Lazy Evaluation First (+3).
Teams using data-analysis-1-lazy-evaluation-first 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-lazy-evaluation-first/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-analysis-1-lazy-evaluation-first Compares
| Feature / Agent | data-analysis-1-lazy-evaluation-first | 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: 1. Lazy Evaluation First (+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. Lazy Evaluation First (+3)
## 1. Lazy Evaluation First
```python
# Prefer lazy operations, collect only when needed
result = (
pl.scan_parquet("data/*.parquet")
.filter(...)
.group_by(...)
.agg(...)
.collect() # Execute at the end
)
```
## 2. Progressive Disclosure in Dashboards
```python
# Start with summary, allow drill-down
st.header("Overview")
show_metrics()
with st.expander("Detailed Analysis"):
show_detailed_charts()
with st.expander("Raw Data"):
st.dataframe(df)
```
## 3. Reproducible Reports
```python
# Include metadata in reports
report_metadata = {
"generated_at": datetime.now().isoformat(),
"data_source": "sales_database",
"date_range": f"{start_date} to {end_date}",
"filters_applied": filters
}
```
## 4. Performance Monitoring
```python
import time
def timed_operation(name):
def decorator(func):
def wrapper(*args, **kwargs):
start = time.time()
result = func(*args, **kwargs)
duration = time.time() - start
logger.info(f"{name} completed in {duration:.2f}s")
return result
return wrapper
return decorator
@timed_operation("Data aggregation")
def aggregate_sales():
...
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