synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill vaexgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/vaex)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/vaex"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/vaex.svg" alt="Measured on agentmods" 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 | $0.01513 |
| Opus 5 | $0.00050 | $0.00757 |
| Sonnet 5 | $0.00020 | $0.00303 |
| Haiku 4.5 | $0.00010 | $0.00151 |
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 4d 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
86% identical to vaex — 55 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 — 182 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.
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
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
2. Data Processing and Manipulation
Perform filtering, create virtual columns, use expressions, and aggregate data without loading everything into memory. Reference references/data_processing.md for:
- Filtering and selections
- Virtual columns and expressions
- Groupby operations and aggregations
- String operations and datetime handling
- Working with missing data
3. Performance and Optimization
Leverage Vaex's lazy evaluation, caching strategies, and memory-efficient operations. Reference references/performance.md for:
- Understanding lazy evaluation
- Using
delay=Truefor batching operations - Materializing columns when needed
- Caching strategies
- Asynchronous operations
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.
- 4d ago First seen · 182 lines · 100 tokens per session scan A 426668e1b819
vaex is a skill published in the GitHub repository synthetic-sciences/openscience (3,501 stars, last pushed today), licensed Apache-2.0. It adds 100 tokens to every session and 1,513 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to vaex, differing in 55 lines, and is treated as a copy.
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