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 autoviz-1-sample-large-datasets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
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
正在显示 SKILL.md
| name | autoviz-1-sample-large-datasets |
| description | Sub-skill of autoviz: 1. Sample Large Datasets (+3). |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
# GOOD: Use sampling for initial exploration
AV.AutoViz(
filename="",
dfte=large_df,
max_rows_analyzed=50000, # Sample for speed
verbose=1
)
# AVOID: Analyzing millions of rows directly
# This will be slow and may crash
# GOOD: Specify target for focused analysis
AV.AutoViz(
filename="",
dfte=df,
depVar="target_column", # Enables target-specific charts
verbose=1
)
# LESS USEFUL: No target specified
# Still works but misses target-related insights
# For presentations: PNG
chart_format="png"
# For reports/web: HTML
chart_format="html"
# For notebooks: server or bokeh
chart_format="server"
# For scalable graphics: SVG
chart_format="svg"
# GOOD: Save to organized directory
import os
output_dir = f"eda_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
os.makedirs(output_dir, exist_ok=True)
AV.AutoViz(
filename="",
dfte=df,
save_plot_dir=output_dir,
chart_format="png"
)