dask
Scales pandas, NumPy and custom Python workflows beyond memory or across clusters with Dask: DataFrames, Arrays, Bags, Futures, schedulers and diagnostics.
- Kind
- Skill
- Works with
- Claude Code, Codex, Cursor, Gemini / Antigravity
- Languages
- Python
- Source
- Scientific Agent Skills (K-Dense) @ 5f2bc0341354
- Path
- skills/dask/SKILL.md
- Jobs
- Implementation
- Topics
- Data analysis & BI
Credit
- Made by
- K-Dense-AI
- Original
- github.com/K-Dense-AI/scientific-agent-skills/blob/5f2bc03413…
- License
- MIT
Listed by Skill Manifest; not made by us.
InstallInstall it by hand, following the source.
gh skill install K-Dense-AI/scientific-agent-skills dask
Install factsScanned 2 Nov 2026
- Adds
- .claude/skills/dask/ (the skill folder)
- 7 file(s) the skill refers to
- Runs scripts
- Yes
- allowed-tools includes Bash
- shell block in the text
- Uses the network
- Yes
- downloads Python packages
- Needs secrets
- None found
- Source revision
- 5f2bc0341354
- Licence
- MIT
- Last activity
- 26 Oct 2026
- Review
- Reviewed
Found by a static scan of the stored text; nothing is run. “None found” is not a guarantee, so read what you install before you run it.
- Only the main file is read; other files in the folder are not scanned
# 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](https://docs.dask.org/en/stable/). 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 ```bash uv pip install "dask[array,dataframe]==2026.8.0" ``` For a typical pandas/NumPy workflow with the distributed scheduler and dashboard: ```bash uv pip install "dask[complete]==2026.8.0" ``` Remote object storage (requires provider credentials for private data): ```bash uv pip install s3fs # s3:// paths uv pip install gcsfs # gs:// paths …