dask

Trigger phrases

Phrases that activate this skill when typed to Claude Code:

• my data is too big for pandas
• dask dataframe
• parallel processing with dask
• distributed computation
• out-of-core computation

What it does

dask is a Claude Code skill from K-Dense AI’s scientific-agent-skills repo. It turns Claude into a Dask expert for scaling Python data workflows — Dask DataFrames for larger-than-RAM tabular data, Dask Arrays for chunked NumPy computations, the Dask distributed scheduler for cluster parallelism, and Dask-ML for distributed machine learning. Dask’s API mirrors pandas and NumPy closely, minimizing rewrite effort when scaling existing code.

A session produces Dask-based Python code that runs the same logical operations as pandas/NumPy code but on data that exceeds available RAM, with guidance on chunk sizing and scheduler selection.

When to use it

Reach for it when:

• Your data doesn’t fit in RAM but you want to keep the pandas-style API rather than rewriting to Spark or SQL
• You need to process a directory of large files in parallel (reading, filtering, aggregating) on a single machine or a cluster
• You’re scaling an existing pandas-based ML preprocessing pipeline to larger data without changing the model code

When not to reach for it:

• Data fits in RAM and speed is the goal — use polars for much better single-machine performance
• You need an interactive analytics database — consider DuckDB or similar

Install

Copy the SKILL.md from K-Dense AI’s dask folder into .claude/skills/dask/ in your project.

Trigger phrases: “my data is too big for pandas”, “dask dataframe”, “parallel processing with dask”, “out-of-core computation”.

What a session looks like

A typical session has three phases:

1. Scale assessment. Describe your data size, file format, and the operations you need. Claude recommends whether a Dask DataFrame, Dask Array, or Dask Delayed is the right abstraction and advises on chunk size.
2. Code migration or generation. Claude writes or adapts the pandas/NumPy code to Dask equivalents, with .compute() calls placed at the final output step rather than intermediate steps.
3. Scheduler configuration. Claude sets up the appropriate scheduler — local multi-threaded, local multi-process, or distributed cluster — and adds progress monitoring with the Dask dashboard.

Receipts

Where it works well:

• Reading and filtering 50+ GB CSV or Parquet file collections where chunked reads allow the full dataset to be processed without loading everything into memory
• Embarrassingly parallel file processing (e.g., convert a folder of 10,000 CSV files to Parquet) where Dask’s task graph trivially parallelizes the loop

Where it backfires:

• Operations that require full data shuffles (groupby with aggregation across all chunks, joins without aligned partitions) incur significant overhead compared to in-memory pandas on the same data
• Debugging Dask task graphs when something goes wrong requires understanding deferred execution in a way that pandas users often find unintuitive

Pattern that works: profile the bottleneck operation in pandas on a 10% sample first; if the operation vectorizes cleanly (filter, map, column-wise transform), Dask scales it well — if it requires global state (ranking, mode), the overhead may not be worth it.

Source and attribution

Originally authored by K-Dense Inc.. The canonical SKILL.md lives in the dask folder of their public scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner’s perspective. For the formal spec and any updates, defer to the source repo.