用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/vamseeachanta/workspace-hub --skill data-analysis-1-lazy-evaluation-first命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 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 |
# Prefer lazy operations, collect only when needed
result = (
pl.scan_parquet("data/*.parquet")
.filter(...)
.group_by(...)
.agg(...)
.collect() # Execute at the end
)
# 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)
# 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
}
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():
...