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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.

87

1.70x
Quality

86%

Does it follow best practices?

Impact

87%

1.70x

Average score across 3 eval scenarios

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Quality

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 a strong skill description that clearly identifies its domain (astronomy/astrophysics Python library), lists specific capabilities comprehensively, and provides explicit 'Use when' guidance with natural trigger terms. The description uses proper third-person voice and covers both what the skill does and when it should be selected. The only minor note is slight redundancy between the capability listing and the trigger clause, but this actually reinforces discoverability.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions and domains: celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, WCS, coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, and astronomical data processing.

3 / 3

Completeness

Clearly answers both 'what' (comprehensive Python library for astronomy covering coordinates, units, FITS, cosmology, etc.) and 'when' with explicit trigger guidance ('Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing').

3 / 3

Trigger Term Quality

Excellent coverage of natural terms a user would say: 'astronomical data', 'celestial coordinates', 'FITS files', 'cosmological calculations', 'WCS', 'coordinate transformations', 'unit conversions', 'time scale conversions'. These are the exact terms astronomers and data scientists would use.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive niche in astronomy/astrophysics with domain-specific triggers like 'FITS files', 'celestial coordinates', 'cosmological distance calculations', and 'WCS' that are unlikely to conflict with other skills.

3 / 3

Total

12

/

12

Passed

Implementation

72%

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 progressive disclosure and strong actionability through concrete code examples. Its main weakness is verbosity — there's substantial redundancy between the overview, 'When to Use' section, core capabilities summaries, and workflow examples. Trimming the redundant sections and removing advice Claude already knows (basic best practices) would significantly improve token efficiency.

Suggestions

Remove or drastically shorten the 'When to Use This Skill' section since it largely duplicates the Overview and is already covered by the skill's YAML description/metadata.

Trim the 'Core Capabilities' key operations bullet lists since they mostly restate what the reference files cover — a one-line summary per module plus the 'See:' link would suffice.

Remove generic best practices Claude already knows (e.g., 'use context managers', 'prefer arrays over loops') and keep only astronomy-specific guidance.

DimensionReasoningScore

Conciseness

The skill contains significant redundancy: the 'When to Use This Skill' section largely repeats the overview, and the 'Core Capabilities' section restates what's already shown in Quick Start and later in Common Workflows. The 'Key operations' bullet lists for each module are somewhat verbose given that they point to reference files anyway. The 'Best Practices' section includes advice Claude already knows (use context managers, prefer arrays over loops).

2 / 3

Actionability

The skill provides fully executable, copy-paste ready code examples throughout — Quick Start, coordinate conversion, FITS file reading, cosmological calculations, and catalog cross-matching are all concrete and complete. Installation commands are specific (uv pip install).

3 / 3

Workflow Clarity

The Common Workflows section provides clear multi-step examples but lacks validation checkpoints. For instance, the FITS file workflow doesn't verify file existence or handle errors, and the cross-matching workflow doesn't validate that coordinate columns exist. For a library skill these are less critical than for destructive operations, but the workflows are presented as linear sequences without any error handling or verification steps.

2 / 3

Progressive Disclosure

Excellent progressive disclosure structure: a concise Quick Start, mid-level Core Capabilities summaries with clear 'See:' pointers to reference files, and a well-organized Reference Files section at the end. All references are one level deep and clearly signaled.

3 / 3

Total

10

/

12

Passed

Validation

90%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

10

/

11

Passed

Repository
K-Dense-AI/claude-scientific-skills
Reviewed

Table of Contents

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