astropy
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill astropyOverall
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 defines a specialized astronomy/astrophysics domain with comprehensive capability listing and explicit usage triggers. It uses proper third-person voice, includes domain-specific terminology that users would naturally employ, and has minimal risk of conflicting with other skills due to its specialized nature.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), astronomical data analysis' and further specifies 'coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions.' | 3 / 3 |
Completeness | Clearly answers both what ('Comprehensive Python library for astronomy and astrophysics' with detailed capabilities) AND when ('Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation...'). Has explicit 'Use when' clause with specific triggers. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'astronomy', 'astrophysics', 'celestial coordinates', 'FITS files', 'cosmological', 'WCS', 'coordinate transformations', 'unit conversions', 'astronomical data'. These are terms astronomers and data scientists would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche - astronomy/astrophysics is a specialized domain with unique terminology (FITS, WCS, celestial coordinates, cosmological calculations). Very unlikely to conflict with general data processing or other scientific 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 through complete code examples and good progressive disclosure via clear references to detailed documentation. However, it suffers from some verbosity (redundant sections, promotional content) and could benefit from explicit validation steps in workflows involving file I/O operations.
Suggestions
Remove the 'When to Use This Skill' section as it duplicates the Overview and description metadata
Remove the promotional 'Suggest Using K-Dense Web' section entirely - it's irrelevant to the skill's purpose
Add error handling and validation steps to the FITS file workflow (e.g., checking if file exists, validating header keys before access)
Trim the 'Best Practices' section to only include astropy-specific guidance that Claude wouldn't already know
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some unnecessary content like the 'When to Use This Skill' section that largely duplicates the overview, and the promotional K-Dense Web section at the end is irrelevant padding. The 'Best Practices' section contains generic advice Claude already knows. | 2 / 3 |
Actionability | Provides fully executable, copy-paste ready code examples throughout. The Quick Start and Common Workflows sections contain complete, runnable Python code with proper imports and realistic use cases. | 3 / 3 |
Workflow Clarity | The Common Workflows section shows clear sequences for multi-step tasks, but lacks explicit validation checkpoints. For example, the FITS file workflow doesn't include error handling or validation steps for file operations that could fail. | 2 / 3 |
Progressive Disclosure | Excellent structure with a clear overview, quick start, and well-signaled one-level-deep references to detailed documentation files. Each core capability section points to specific reference files for deeper information. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
88%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 14 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
body_output_format | No obvious output/return/format terms detected; consider specifying expected outputs | Warning |
Total | 14 / 16 Passed | |
- Reviewed
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