dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
npx skills add K-Dense-AI/scientific-agent-skills --skill dask --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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](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>=2025.1" ``` For a typical pandas/NumPy workflow with the distributed scheduler and dashboard: ```bash uv pip install "dask[complete]" ``` Remote object storage (S3, GCS, Azure): ```bash 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
- Overview
- Quick Start
- Installation
- When to Use This Skill
- Core Capabilities
- 1. DataFrames - Parallel Pandas Operations
- 2. Arrays - Parallel NumPy Operations
- 3. Bags - Parallel Processing of Unstructured Data
- 4. Futures - Task-Based Parallelization
- 5. Schedulers - Execution Backends
- Best Practices
- Start with Simpler Solutions
- Critical Performance Rules
- Common Workflow Patterns
uv pip install "dask>=2025.1" uv pip install "dask[complete]" uv pip install s3fs # s3:// paths uv pip install gcsfs # gs:// paths
What does the dask skill do?
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill dask --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.