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 agentmods add skills/g1joshi/agent-skills/dasknpx skills add G1Joshi/Agent-Skills --skill daskgit clone --depth 1 https://github.com/G1Joshi/Agent-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/g1joshi/agent-skills/dask)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/dask"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/dask.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 | $0.00014 | $0.00257 |
| Opus 5 | $0.00007 | $0.00129 |
| Sonnet 5 | $0.00003 | $0.00051 |
| Haiku 4.5 | $0.00001 | $0.00026 |
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 6d 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.
What it actually says
Dask
Dask scales Python. It looks like Pandas/NumPy but runs on clusters. 2025 updates focus on High Performance Shuffle and GPU integration.
When to Use
- Big Data: When data > RAM but < BigQuery scale.
- Cluster Computing: Utilizing a Kubernetes cluster for Python functions.
- Xarray: Backend for geospatial data.
Core Concepts
Collections
dask.dataframe, dask.array, dask.bag.
Scheduler
Decides where to run tasks (Local Threads, Processes, or Distributed Cluster).
Dashboard
Real-time visualization of task progress (port 8787).
Best Practices (2025)
Do:
- Use
dask-expr: The new query optimization engine for Dask DataFrames. - Use Parquet: CSVs are distinctively slow in distributed settings.
Don't:
- Don't use for small data: The overhead of the scheduler makes it slower than Pandas for <1GB.
References
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.
- 6d ago First seen · 44 lines · 14 tokens per session scan A 345b6ee9dfb4
dask is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 14 tokens to every session and 257 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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