vaex
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
- License 许可
- MIT
- 最近更新
- 2026-11-09
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- handoff
- description
- Compact the current conversation into a handoff document for another agent to pick up.
- argument-hint
- What will the next session be used for?
- disable-model-invocation
- true
02 资源文件 7
- 参考文档agents/openai.yaml141 B
- 参考文档references/core_dataframes.md7.7 KB
- 参考文档references/data_processing.md12.2 KB
- 参考文档references/io_operations.md14.6 KB
- 参考文档references/machine_learning.md15.6 KB
- 参考文档references/performance.md13.4 KB
- 参考文档references/visualization.md12.9 KB
03 SKILL.md 正文
Write a handoff document summarising the current conversation so a fresh agent can continue the work. Save to the temporary directory of the user's OS - not the current workspace.
Include a "suggested skills" section in the document, which suggests skills that the agent should invoke.
Do not duplicate content already captured in other artifacts (specs, plans, ADRs, issues, commits, diffs). Reference them by path or URL instead.
Redact any sensitive information, such as API keys, passwords, or personally identifiable information.
If the user passed arguments, treat them as a description of what the next session will focus on and tailor the doc accordingly.
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