Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dralkh/seerai --skill vaexgit clone --depth 1 https://github.com/dralkh/seeraiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dralkh/seerai/vaex)<a href="https://agentmods.dev/skills/dralkh/seerai/vaex"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/vaex/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dralkh/seerai/vaex"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/vaex.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00100 | $0.01908 |
| Opus 5.5 | $0.00040 | $0.00763 |
| Sonnet 5.5 | $0.00020 | $0.00382 |
| Haiku 4.5 | $0.00010 | $0.00191 |
Grade A, and why
vaex scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to vaex — 275 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vaex
Overview
Vaex is a high-performance Python library designed for lazy, out-of-core DataFrames to process and visualize tabular datasets that are too large to fit into RAM. Vaex can process over a billion rows per second, enabling interactive data exploration and analysis on datasets with billions of rows.
Installation
Install the full meta-package (recommended):
uv pip install vaex
Minimal install (pick only what you need):
uv pip install vaex-core vaex-viz vaex-hdf5 vaex-ml
The vaex package is a meta-package that pulls in vaex-core, vaex-viz, vaex-hdf5, vaex-ml, and other sub-packages. Arrow support is built into vaex-core (the separate vaex-arrow package is deprecated). vaex-distributed is deprecated in favor of vaex-enterprise.
Version notes (vaex 4.19.0+): Python 3.12 and NumPy v2 require vaex >= 4.19.0. On Windows, you may need Python dev headers to build the annoy dependency.
When to Use This Skill
Use Vaex when:
- Processing tabular datasets larger than available RAM (gigabytes to terabytes)
- Performing fast statistical aggregations on massive datasets
- Creating visualizations and heatmaps of large datasets
- Building machine learning pipelines on big data
- Converting between data formats (CSV, HDF5, Arrow, Parquet)
- Needing lazy evaluation and virtual columns to avoid memory overhead
- Working with astronomical data, financial time series, or other large-scale scientific datasets
Vaex vs alternatives: Use polars when data fits in RAM and you need maximum in-memory speed. Use dask when you need distributed pandas/NumPy across a cluster. Use vaex for single-machine, out-of-core analytics on tabular data that exceeds RAM via memory-mapped HDF5/Arrow files.
Core Capabilities
Vaex provides six primary capability areas, each documented in detail in the references directory:
1. DataFrames and Data Loading
Load and create Vaex DataFrames from various sources including files (HDF5, CSV, Arrow, Parquet), pandas DataFrames, NumPy arrays, and dictionaries. Reference references/core_dataframes.md for:
- Opening large files efficiently
- Converting from pandas/NumPy/Arrow
- Working with example datasets
- Understanding DataFrame structure
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 203 lines · 100 tokens per session scan A b3d4cf8f0df4
vaex is a skill published in the GitHub repository dralkh/seerai (94 stars, last pushed 21d ago), licensed MIT. It adds 100 tokens to every session and 1,908 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to vaex, differing in 275 lines, and is treated as a copy.
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