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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill daskOverall
score
86%
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that clearly articulates what the skill does (distributed computing for pandas/NumPy), when to use it (scaling beyond memory/across clusters), and importantly differentiates itself from related tools (vaex, polars). The description uses third person voice, includes natural trigger terms, and provides specific use cases without being verbose.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'larger-than-RAM pandas/NumPy workflows', 'scale existing pandas/NumPy code beyond memory or across clusters', 'parallel file processing', 'distributed ML', 'integration with existing pandas code'. | 3 / 3 |
Completeness | Clearly answers both what ('Distributed computing for larger-than-RAM pandas/NumPy workflows') and when ('Use when you need to scale existing pandas/NumPy code beyond memory or across clusters'), with explicit trigger guidance and even differentiation from alternatives. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'distributed computing', 'larger-than-RAM', 'pandas', 'NumPy', 'memory', 'clusters', 'parallel file processing', 'distributed ML', plus comparison terms 'vaex' and 'polars'. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche (Dask-style distributed pandas/NumPy), explicitly differentiates from vaex (out-of-core single machine) and polars (in-memory speed), making it very unlikely to conflict with similar data processing skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
73%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with excellent actionability and progressive disclosure. The code examples are executable and comprehensive, and the reference file organization is clear. However, it suffers from some verbosity in introductory sections and lacks explicit validation checkpoints in workflows. The promotional K-Dense section at the end is inappropriate for a skill file.
Suggestions
Remove or significantly trim the 'When to Use This Skill' section as it duplicates information Claude can infer from context
Remove the promotional 'Suggest Using K-Dense Web' section entirely - this is inappropriate content for a technical skill file
Add explicit validation steps to workflow patterns (e.g., 'Verify row count after filtering', 'Check for null values before aggregation')
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some unnecessary explanatory text (e.g., 'Dask is a Python library for parallel and distributed computing that enables three critical capabilities'). The 'When to Use This Skill' section largely duplicates information Claude would infer. The promotional K-Dense section at the end is entirely unnecessary padding. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples throughout. Each component section includes concrete Python code with proper imports, and the workflow patterns section demonstrates real-world usage with complete, runnable examples. | 3 / 3 |
Workflow Clarity | Multi-step workflows are presented (ETL Pipeline, Iterative Development Workflow) but lack explicit validation checkpoints. The debugging section mentions checking for issues but doesn't provide explicit validate-fix-retry loops for operations that could fail or produce incorrect results. | 2 / 3 |
Progressive Disclosure | Excellent structure with clear overview content and well-signaled one-level-deep references to detailed documentation files (references/dataframes.md, references/arrays.md, etc.). Each component section provides quick guidance with explicit pointers to comprehensive reference files. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
94%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 15 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 15 / 16 Passed | |
- Reviewed
Table of Contents
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