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/iktinah --skill daskgit clone --depth 1 https://github.com/dralkh/iktinahWrote 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/iktinah/dask)<a href="https://agentmods.dev/skills/dralkh/iktinah/dask"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/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/dralkh/iktinah/dask"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/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 | $0.00069 | $0.03616 |
| Opus 5.5 | $0.00028 | $0.01446 |
| Sonnet 5.5 | $0.00014 | $0.00723 |
| Haiku 4.5 | $0.00007 | $0.00362 |
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 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
100% identical to dask — 93 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 — 481 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
Dask scales from laptops (processing ~100 GiB) to clusters (processing ~100 TiB) while maintaining familiar Python APIs.
Current upstream: dask 2026.3.0 (PyPI, March 2026). 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>=2025.1"
For a typical pandas/NumPy workflow with the distributed scheduler and dashboard:
uv pip install "dask[complete]"
Remote object storage (S3, GCS, Azure):
uv pip install s3fs # s3:// paths
uv pip install gcsfs # gs:// paths
Requires Python 3.10+ (3.9 support dropped in 2024.12). DataFrame I/O requires PyArrow 16+ (as of dask 2026.1.2).
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
Core Capabilities
Dask provides five main components, each suited to different use cases:
1. DataFrames - Parallel Pandas Operations
Purpose: Scale pandas operations to larger datasets through parallel processing.
When to Use:
- Tabular data exceeds available RAM
- Need to process multiple CSV/Parquet files together
- Pandas operations are slow and need parallelization
- Scaling from pandas prototype to production
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 · 481 lines · 69 tokens per session scan A fffb66c082fb
dask is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 3mo ago), licensed MIT. It adds 69 tokens to every session and 3,616 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 93 lines, and is treated as a copy.
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