---
name: "dask-distributed-dataframe"
description: "Scale Pandas DataFrames across multi-core CPUs and distributed compute clusters with Dask."
metadata:
  author: "Dask"
  version: "1.0.0"
  runtime_compatibility:
    - "codex"
    - "claude-code"
    - "cursor"
    - "windsurf"
triggers:
  - "use dask dataframe"
  - "scale pandas across cores"
  - "run distributed dask cluster"
permissions:
  fs_read: true
  fs_write: "workspace"
  shell_exec: false
  net_access: false
---

# Dask Distributed DataFrame & Parallel Tasks

Parallelizes Python data science workloads using Dask: Dask DataFrame, delayed task graphs, distributed Client clusters, partition optimization, and chunked Parquet writing.

## When to Invoke
Invoke this skill whenever you need to:
- use dask dataframe
- scale pandas across cores
- run distributed dask cluster

## Execution SOP & Rules
1. Initialize cluster connection using from dask.distributed import Client; client = Client().
2. Read partitioned datasets with dd.read_parquet('data/*.parquet').
3. Target partition sizes between 100MB and 250MB using df.repartition(partition_size='150MB').
4. Trigger computation only when necessary with df.compute() to avoid premature memory consumption.

## Verification Checklist
- Run local linting, syntax validation, and type-checks before reporting completion.
- Ensure all created or modified files adhere to workspace boundaries and avoid uncommitted secrets.
- Verify tests or build commands exit cleanly with return code 0.