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vaex

Skill3 starsChanged 8 months agoMITView on GitHub

Process very large tabular data for CS experiments and engineering logs without loading everything into RAM. Use when working with massive result tables, telemetry, predictions, feature exports, ranking data, or other large CSV, HDF5, Arrow, or Parquet files that need fast lazy aggregation and filtering.

11 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 11 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when dataset size exceeds comfortable in-memory workflows but you still want DataFrame-style analysis.

    line 8
  2. Distinctive:

    Keep transformations lazy as long as possible.

    line 29
  3. Distinctive:

    Use Vaex for big-table filtering and aggregation, then hand smaller outputs to plotting or modeling tools.

    line 31
  4. Distinctive:

    When the user asks for Vaex help without more detail, return:

    line 36

“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Vaex

Use this skill when dataset size exceeds comfortable in-memory workflows but you still want DataFrame-style analysis.

Common CS Uses

  • analyze large experiment logs or sweep outputs
  • inspect ranking or retrieval results at scale
  • aggregate telemetry, latency, or failure-event tables
  • process feature exports or prediction dumps too large for RAM
  • build lightweight big-table analysis pipelines

Local References

  • references/core_dataframes.md
  • references/data_processing.md
  • references/io_operations.md
  • references/performance.md
  • references/visualization.md
  • references/machine_learning.md

Working Rules

  • Keep transformations lazy as long as possible.
  • Materialize only the slices needed for downstream modeling or plotting.
  • Use Vaex for big-table filtering and aggregation, then hand smaller outputs to plotting or modeling tools.
  • Be explicit about file formats and column types early to avoid costly conversions.

Default Output

When the user asks for Vaex help without more detail, return:

  1. recommended data-loading strategy
  2. likely lazy operations to use
  3. performance and memory notes
  4. downstream handoff advice

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