How do I install the Large Data With Dask skill?

Run npx skillmds@latest add oimiragieo/large-data-with-dask in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Large Data With Dask safe to use?

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Is Large Data With Dask free to use?

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Who published Large Data With Dask?

oimiragieo (@oimiragieo) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Large Data With Dask by oimiragieo — Agent Skill

Name: Large Data With Dask
Rating: 4.2 (5 reviews)
Author: oimiragieo

File contents

Large Data With Dask Skill

Consider using dask for larger-than-memory datasets.

Iron Laws

ALWAYS call dask.compute() only once at the end of a pipeline — multiple intermediate compute() calls break the lazy evaluation graph and eliminate Dask's ability to fuse and parallelize operations.
NEVER use df.apply(lambda ...) with Dask DataFrames for element-wise operations — Pandas-style apply forces row-by-row Python execution that bypasses Dask's vectorized C extensions and is slower than single-threaded Pandas.
ALWAYS specify partition sizes explicitly when reading large datasets (blocksize= for CSV, chunksize= for Parquet) — auto-detected partition sizes frequently produce thousands of tiny partitions (slow scheduler overhead) or a single giant partition (no parallelism).
NEVER call len(df) or df.shape on a Dask DataFrame without wrapping in compute() — these trigger immediate full dataset computation and negate lazy evaluation.
ALWAYS use dask.distributed.Client for multi-machine or CPU-bound workloads — the default threaded scheduler serializes Python-heavy operations due to the GIL; the distributed scheduler bypasses this.

Anti-Patterns

Anti-Pattern	Why It Fails	Correct Approach
Multiple `compute()` calls in pipeline	Breaks lazy graph; forces data to materialize and re-partition at each call	Build complete computation graph first; call `compute()` once at the end
`df.apply(lambda ...)` on large DataFrames	Row-by-row Python; GIL contention; slower than equivalent Pandas on single core	Use vectorized Dask operations (`map_partitions`, `assign`, arithmetic operators)
Default blocksize on large CSV files	128MB default creates thousands of partitions for 100GB files; scheduler overhead dominates	Set `blocksize="256MB"` or `blocksize="1GB"` for large files; profile optimal size
`len(df)` without `compute()`	Triggers full dataset read and count; defeats lazy evaluation	Use `df.shape[0].compute()` explicitly; only compute when size is truly needed
Threaded scheduler for CPU-bound work	Python GIL serializes CPU computation across threads; no true parallelism	Use `dask.distributed.LocalCluster()` or process-based scheduler for CPU tasks

Memory Protocol (MANDATORY)

Before starting:

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.

---name: large-data-with-daskdescription: Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.---# 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>## Iron Laws1. **ALWAYS** call `dask.compute()` only once at the end of a pipeline — multiple intermediate `compute()` calls break the lazy evaluation graph and eliminate Dask's ability to fuse and parallelize operations.2. **NEVER** use `df.apply(lambda ...)` with Dask DataFrames for element-wise operations — Pandas-style `apply` forces row-by-row Python execution that bypasses Dask's vectorized C extensions and is slower than single-threaded Pandas.3. **ALWAYS** specify partition sizes explicitly when reading large datasets (`blocksize=` for CSV, `chunksize=` for Parquet) — auto-detected partition sizes frequently produce thousands of tiny partitions (slow scheduler overhead) or a single giant partition (no parallelism).4. **NEVER** call `len(df)` or `df.shape` on a Dask DataFrame without wrapping in `compute()` — these trigger immediate full dataset computation and negate lazy evaluation.5. **ALWAYS** use `dask.distributed.Client` for multi-machine or CPU-bound workloads — the default threaded scheduler serializes Python-heavy operations due to the GIL; the distributed scheduler bypasses this.## Anti-Patterns| Anti-Pattern | Why It Fails | Correct Approach || ------------------------------------------ | ------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- || Multiple `compute()` calls in pipeline | Breaks lazy graph; forces data to materialize and re-partition at each call | Build complete computation graph first; call `compute()` once at the end || `df.apply(lambda ...)` on large DataFrames | Row-by-row Python; GIL contention; slower than equivalent Pandas on single core | Use vectorized Dask operations (`map_partitions`, `assign`, arithmetic operators) || Default blocksize on large CSV files | 128MB default creates thousands of partitions for 100GB files; scheduler overhead dominates | Set `blocksize="256MB"` or `blocksize="1GB"` for large files; profile optimal size || `len(df)` without `compute()` | Triggers full dataset read and count; defeats lazy evaluation | Use `df.shape[0].compute()` explicitly; only compute when size is truly needed || Threaded scheduler for CPU-bound work | Python GIL serializes CPU computation across threads; no true parallelism | Use `dask.distributed.LocalCluster()` or process-based scheduler for CPU tasks |## Memory Protocol (MANDATORY)**Before starting:**```bashcat .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.

Frequently asked questions

Large Data With Dask