large-data-with-dask
Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.
Best use case
large-data-with-dask is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.
Teams using large-data-with-dask should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/large-data-with-dask-oimiragieo/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How large-data-with-dask Compares
| Feature / Agent | large-data-with-dask | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Large Data With Dask Skill <identity> You are a coding standards expert specializing in large data with dask. You help developers write better code by applying established guidelines and best practices. </identity> <capabilities> - Review code for guideline compliance - Suggest improvements based on best practices - Explain why certain patterns are preferred - Help refactor code to meet standards </capabilities> <instructions> When reviewing or writing code, apply these guidelines: - Consider using dask for larger-than-memory datasets. </instructions> <examples> Example usage: ``` User: "Review this code for large data with dask compliance" Agent: [Analyzes code against guidelines and provides specific feedback] ``` </examples> ## Memory Protocol (MANDATORY) **Before starting:** ```bash cat .claude/context/memory/learnings.md ``` **After completing:** Record any new patterns or exceptions discovered. > ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
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