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 Lzy599775/agent-auto-sci-skills --skill daskgit clone --depth 1 https://github.com/Lzy599775/agent-auto-sci-skillsWrote 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/lzy599775/agent-auto-sci-skills/dask)<a href="https://agentmods.dev/skills/lzy599775/agent-auto-sci-skills/dask"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/dask/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/lzy599775/agent-auto-sci-skills/dask"><img src="https://agentmods.dev/badge/skills/lzy599775/agent-auto-sci-skills/dask.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 | $0.00067 | $0.04075 |
| Opus 5.5 | $0.00027 | $0.01630 |
| Sonnet 5.5 | $0.00013 | $0.00815 |
| Haiku 4.5 | $0.00007 | $0.00407 |
Grade A, and why
dask 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
100% identical to dask — 0 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 — 494 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dask
Overview
Dask is a Python library for parallel and distributed computing that enables three critical capabilities:
- Larger-than-memory execution on single machines for data exceeding available RAM
- Parallel processing for improved computational speed across multiple cores
- Distributed computation supporting terabyte-scale datasets across multiple machines
Capacity depends on partition sizes, intermediate data, concurrency, storage, and available memory.
Reviewed release: dask/distributed 2026.8.0 (September 30, 2026 review). Docs: docs.dask.org. Since 2025.1.0, the expression-based DataFrame API with query planning is the only implementation — do not install dask-expr separately or set dataframe.query-planning: False.
Quick Start
Installation
uv pip install "dask[array,dataframe]==2026.8.0"
For a typical pandas/NumPy workflow with the distributed scheduler and dashboard:
uv pip install "dask[complete]==2026.8.0"
Remote object storage (requires provider credentials for private data):
uv pip install s3fs # s3:// paths
uv pip install gcsfs # gs:// paths
Requires Python 3.10+, pandas 2+, PyArrow 16+. Zarr, HDF5, SciPy, Xarray, Dask-ML, and cluster deployment packages are separate optional dependencies. See review and validation for tested versions, source links, and limits. File paths and undefined application functions below are illustrative. Put process scheduler/Client() execution in a main() guarded by if __name__ == "__main__": in scripts; close clients and clusters afterward.
When to Use This Skill
This skill should be used when:
- Process datasets that exceed available RAM
- Scale pandas or NumPy operations to larger datasets
- Parallelize computations for performance improvements
- Process multiple files efficiently (CSVs, Parquet, JSON, text logs)
- Build custom parallel workflows with task dependencies
- Distribute workloads across multiple cores or machines
What ships with it
7 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 Changed · -5 lines · -2 tokens per session 3b563a98f4cb
- 1mo ago Changed · +16 lines 8419784c1256
- 1mo ago First seen · 483 lines · 69 tokens per session scan A bfac81cda498
dask is a skill published in the GitHub repository Lzy599775/agent-auto-sci-skills (2 stars, last pushed today), licensed MIT. It adds 67 tokens to every session and 4,075 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to dask, differing in 0 lines, and is treated as a copy.
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