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astropy

Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology. Use when implementing or debugging astronomical data analysis code with Astropy.

72

Quality

89%

Does it follow best practices?

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SKILL.md
Quality
Evals
Security

Quality

Content

86%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-organized, executable library-reference skill that appropriately splits content across one-level-deep reference files. Slight verbosity in restated best-practice bullets and the absence of explicit validation checkpoints in workflows are the only gaps.

Suggestions

Trim Best Practices and 'Key operations' bullets that restate knowledge Claude already has (e.g., 'Always use units', 'Use context managers for FITS files') to improve conciseness.

Add explicit validation checkpoints where workflows touch shared state (e.g., verify WCS validity before transformations, confirm cosmology results have expected units) to strengthen workflow clarity.

Consider adding common file-extension triggers (e.g., '.fits') to the description for fuller trigger-term coverage.

DimensionReasoningScore

Conciseness

Mostly lean with executable code and detail delegated to references, but some 'Key operations' bullet lists and Best Practices restate knowledge Claude already has (e.g., 'Always use units').

4 / 5

Actionability

Copy-paste ready code in Quick Start and four Common Workflows, plus concrete pinned install commands, covering the common cases fully.

5 / 5

Workflow Clarity

Workflows are concrete and correctly sequenced (e.g., AltAz transform supplies time and location), but lack explicit validate→fix→retry checkpoints; acceptable since this is a library-reference skill, not a destructive/batch process.

4 / 5

Progressive Disclosure

Clear overview with well-signaled one-level-deep references ('**See:** references/units.md'), all referenced files present, and a dedicated Reference Files section for navigation.

5 / 5

Total

18

/

20

Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, concrete description that names the library, enumerates its capability domains, and provides an explicit 'Use when' trigger. Only minor weakness is the absence of file-extension synonyms like '.fits' in the trigger terms.

DimensionReasoningScore

Specificity

Lists multiple concrete capability domains ('units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology'), giving comprehensive coverage rather than vague abstraction.

5 / 5

Completeness

Explicitly answers both 'what' (core library with the listed capabilities) and 'when' ('Use when implementing or debugging astronomical data analysis code with Astropy').

5 / 5

Trigger Term Quality

Strong natural terms ('astronomy and astrophysics workflows', 'Astropy APIs', 'FITS I/O') but lacks common synonyms and file extensions like '.fits' that users would naturally say.

4 / 5

Distinctiveness Conflict Risk

A clear Astropy/astronomy niche with a named library and distinct triggers, giving minimal overlap risk with other skills.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

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

Table of Contents

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