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Agent skill

Array Workflows

by VectorSpaceLab in VectorSpaceLab/AREX-Skill

A skill your agent uses when working with Dask Array: chunked NumPy-like arrays, dask.array.fromarray, chunk planning, slicing, mapblocks, blockwise, reductions, overlap, rechunking, generalized…

BSD-3-ClauseAuto-check passedData & Analytics

Install Array Workflows

skills CLI
$ npx skills add VectorSpaceLab/AREX-Skill --skill array-workflows -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install VectorSpaceLab/AREX-Skill array-workflows --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/dask/sub-skills/array-workflows .claude/skills/array-workflows && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
array-workflows
GitHub stars
328
Token cost
~839 tokens
SKILL.md length
348 words
Files
6 (incl. scripts, references)
Skills in repo
159
Repo updated
First seen
Licence
BSD-3-Clause

At a glance

A skill your agent uses when working with Dask Array: chunked NumPy-like arrays, dask.array.fromarray, chunk planning, slicing, mapblocks, blockwise, reductions, overlap, rechunking, generalized…

  • Works in 5 steps: Identify the source array: in-memory… → Choose chunks before building the graph.… → Keep operations lazy while defining… → …
  • Working with Dask Array: chunked NumPy-like arrays
  • SKILL.md covers Route First, Start Here, Reference Map and Safe Smoke Check
  • Runs Python scripts from its folder; calls python

What it does

Array Workflows is an agent skill from VectorSpaceLab/AREX-Skill. Use when working with Dask Array: chunked NumPy-like arrays, dask.array.fromarray, chunk planning, slicing, mapblocks, blockwise, reductions, overlap, rechunking, generalized ufuncs, random arrays, linalg/FFT/stats, optional array backends, or array query-planning caveats.

Its SKILL.md is about 840 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/api-reference.md`, `references/chunking-and-performance.md` and `references/troubleshooting.md`).

It sits in Data & Analytics, covering DataFrames. It works with Dask and NumPy. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is BSD-3-Clause.

When your agent uses it

  • Working with Dask Array: chunked NumPy-like arrays
  • Dask.array.fromarray
  • Generalized ufuncs
  • Linalg/FFT/stats

Example prompts

  • “/array-workflows”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Identify the source array: in-memory NumPy, HDF5/Zarr/NetCDF-like object with NumPy slicing, delayed image/file loader, random generator…
  2. Choose chunks before building the graph. Prefer chunks that fit comfortably in memory, do meaningful work per task, align with storage…
  3. Keep operations lazy while defining arrays. Do not call compute() or persist() in the middle of graph construction unless the user…
  4. Use high-level operations first: arithmetic/ufuncs, slicing, reductions, stack, concatenate, rechunk, map_blocks, map_overlap, blockwise…
  5. Validate shape, dtype, chunks, and graph size before broad execution; use tiny local examples when diagnosing shape or metadata issues.

What it can do on your machine

Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Array Workflows loads about 839 tokens when it runs, and up to ~7.1k if it reads all its reference files. Until then it costs about 74 tokens; SKILL.md has 348 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~74
When it runs · the whole SKILL.md, loaded when a task matches
~839
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its BSD-3-Clause licence (© VectorSpaceLab). 348 words, ~839 tokens.

Download SKILL.mdSave it as .claude/skills/array-workflows/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
array-workflows
description
Use when working with Dask Array: chunked NumPy-like arrays, `dask.array.from_array`, chunk planning, slicing, `map_blocks`, `blockwise`, reductions, overlap, rechunking, generalized ufuncs, random arrays, linalg/FFT/stats, optional array backends, or array query-planning caveats.
disable-model-invocation
true
metadata.disco-role
operating
license
BSD 3-Clause

Dask Array Workflows

Use this sub-skill for Dask Array workflows where the primary object is dask.array.Array, a lazy blocked array built from NumPy or NumPy-like chunks. Dask Array is best for arrays too large for memory, multi-file/image stacks, blocked numerical algorithms, and NumPy-style code that can run independently on chunks.

Route First

  • For generic graph construction, scheduler choice, compute, persist, annotations, delayed objects, or HighLevelGraph internals, route to ../core-graphs-schedulers/SKILL.md.
  • For converting Dask DataFrame partitions to arrays, unknown row counts from dataframe conversion, or dataframe IO/shuffle decisions, route to ../dataframe-workflows/SKILL.md and return here for array-only operations.
  • For array.chunk-size, array.rechunk.*, array.slicing.split_large_chunks, array.query-planning, CLI inspection, diagnostics, or profiling, route to ../configuration-diagnostics-cli/SKILL.md.

Start Here

  1. Identify the source array: in-memory NumPy, HDF5/Zarr/NetCDF-like object with NumPy slicing, delayed image/file loader, random generator, dataframe conversion, or optional backend such as CuPy or sparse.
  2. Choose chunks before building the graph. Prefer chunks that fit comfortably in memory, do meaningful work per task, align with storage chunks, and match the main access/reduction pattern.
  3. Keep operations lazy while defining arrays. Do not call compute() or persist() in the middle of graph construction unless the user explicitly requests materialization or chunk-size discovery.
  4. Use high-level operations first: arithmetic/ufuncs, slicing, reductions, stack, concatenate, rechunk, map_blocks, map_overlap, blockwise, and apply_gufunc.
  5. Validate shape, dtype, chunks, and graph size before broad execution; use tiny local examples when diagnosing shape or metadata issues.

Reference Map

  • references/api-reference.md lists the core Dask Array APIs, signatures, and when to use each family.
  • references/workflows.md gives task-oriented recipes for creation, slicing, reductions, map_blocks, blockwise, overlap, gufuncs, random, linalg, FFT, stats, and optional backends.
  • references/chunking-and-performance.md explains chunk forms, unknown chunks, storage alignment, rechunk planning, slicing chunk warnings, and query-planning flags.
  • references/troubleshooting.md maps common Dask Array errors and performance symptoms to fixes.
  • scripts/array_smoke.py is a fixture-free smoke check for array creation, chunks, map_blocks, overlap, rechunk, and compute.

Safe Smoke Check

From the sub-skill directory or a copied skill installation, run:

bash
python scripts/array_smoke.py --help
python scripts/array_smoke.py

The script uses only tiny in-memory arrays and asserts chunk metadata plus computed values. It does not require a source checkout, GPU, network, or external data files.

© VectorSpaceLab, BSD-3-Clause. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in skills/repositories/repo-skills/dask/sub-skills/array-workflows of VectorSpaceLab/AREX-Skill.

  • SKILL.md
  • references/api-reference.md
  • references/chunking-and-performance.md
  • references/troubleshooting.md
  • references/workflows.md
  • scripts/array_smoke.py

Open the folder on GitHubat commit ac3fe1a

Compare with similar skills

Array Workflows next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Array Workflows compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Array Workflows this skillVectorSpaceLab/AREX-Skill328—~839Automated safety check: PassBSD-3-Clause
Daskdavila7/claude-code-templates32k11 repos~3.5kAutomated safety check: PassMIT
DaskK-Dense-AI/scientific-agent-skills48k1 repos~4.4kAutomated safety check: NotesBSD-3-Clause
Dask Parallel Computingjaechang-hits/SciAgent-Skills370—~4.1kAutomated safety check: PassBSD-3-Clause
Zarr Pythondavila7/claude-code-templates32k11 repos~5kAutomated safety check: PassMIT
Python Executorcortega26/chile-hub1132 repos~1.5kAutomated safety check: PassMIT

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Works with

Categories

Questions about Array Workflows

What does Array Workflows do?

A skill your agent uses when working with Dask Array: chunked NumPy-like arrays, dask.array.fromarray, chunk planning, slicing, mapblocks, blockwise, reductions, overlap, rechunking, generalized…. Array Workflows is an agent skill from VectorSpaceLab/AREX-Skill.fromarray, chunk planning, slicing, mapblocks, blockwise, reductions, overlap, rechunking, generalized ufuncs, random arrays, linalg/FFT/stats, optional array backends, or array query-planning caveats.

When should I use Array Workflows?

Array Workflows fits situations like: working with Dask Array: chunked NumPy-like arrays; dask.array.fromarray; generalized ufuncs; linalg/FFT/stats.

How do I install Array Workflows in Claude Code?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill array-workflows -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/dask/sub-skills/array-workflows in VectorSpaceLab/AREX-Skill) into .claude/skills/array-workflows in your project. Claude Code loads it when a task matches its description.

How do I install Array Workflows in Codex?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill array-workflows -a codex`. Or copy the skill folder (skills/repositories/repo-skills/dask/sub-skills/array-workflows in VectorSpaceLab/AREX-Skill) into .agents/skills/array-workflows in your project. Codex loads it when a task matches its description.

Can I use Array Workflows in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add VectorSpaceLab/AREX-Skill --skill array-workflows -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/array-workflows, .gemini/skills/array-workflows, .github/skills/array-workflows and .opencode/skills/array-workflows in your project.

What does Array Workflows need to run?

Going by SKILL.md and its folder, Array Workflows needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Array Workflows access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Array Workflows safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Array Workflows use?

Array Workflows is published under the BSD-3-Clause licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Array Workflows use?

About 839 tokens (SKILL.md is roughly 3.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.3k tokens, read only when the agent opens those files.

What are the alternatives to Array Workflows?

Skills that share tags, products or a category with Array Workflows: Dask (davila7/claude-code-templates, 32k stars), Dask (K-Dense-AI/scientific-agent-skills, 48k stars), Dask Parallel Computing (jaechang-hits/SciAgent-Skills, 370 stars) and Zarr Python (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Array Workflows?

VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on September 3, 2026.

Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.