Skip to content
Back to skills

1 Lazy Evaluation First

ASecurity

Sub-skill of data-analysis: 1. Lazy Evaluation First (+3).

  • 17 stars
  • 0 votes
  • 0 copies
  • 2 views
  • Added September 9, 2026
datapythongodatabaseperformance

Security analysis

A100/100

Scanned September 9, 2026

npx -y skills add vamseeachanta/workspace-hub --skill 1-lazy-evaluation-first --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of 1 Lazy Evaluation First?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for 1 Lazy Evaluation First
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/vamseeachanta-1-lazy-evaluation-first/badge)](https://www.skillsdirectory.com/skills/vamseeachanta-1-lazy-evaluation-first)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
SKILL.md
---
name: data-analysis-1-lazy-evaluation-first
description: 'Sub-skill of data-analysis: 1. Lazy Evaluation First (+3).'
version: 1.0.0
category: data
type: reference
scripts_exempt: true
---

# 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():
    ...
```

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.