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.

Quality

89%

Does it follow best practices?

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Securityby

Passed

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Quality

Content

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

A well-structured reference skill: actionable copy-paste examples, clean progressive disclosure to real per-module reference files, and minimal padding. The main gap is the absence of explicit in-flow validation/error-recovery checkpoints in the worked examples.

Suggestions

Add a brief validation/verification step to at least one worked example (e.g., check `hdul.verify()` or confirm WCS validity with `wcs.has_celestial` before transforming) to model error-recovery feedback loops.

Tighten the per-module 'Key operations' bullet lists so they signal rather than restate the corresponding reference file's contents, recovering context budget.

Note prerequisite assumptions in examples (e.g., that 'observation.fits' / 'catalog1.fits' must exist or be created) so the copy-paste examples are fully self-contained.

Dimension	Reasoning	Score
Conciseness	Mostly efficient content with executable code, pointer-driven module sections, and minimal concept-over-explanation; the per-module 'Key operations' bullet lists mildly restate reference-file contents and could be trimmed slightly, keeping it just below the lean anchor.	4 / 5
Actionability	Five copy-paste-ready worked examples (coordinate transforms, FITS I/O, cosmological distances, catalog cross-matching, Quick Start) with real imports and real API calls, plus concrete pinned install commands, covering the common cases.	5 / 5
Workflow Clarity	Worked examples are clearly sequenced step-by-step and the skill is library-reference rather than destructive/batch (so the validation cap does not bind), but the examples lack explicit validation checkpoints or error-recovery feedback loops in-flow.	4 / 5
Progressive Disclosure	Clear overview in SKILL.md with seven well-signaled, one-level-deep references to real bundle files (each module section uses 'See: references/X.md for ...'), plus a consolidated Reference Files list, giving easy navigation with no nested references.	5 / 5
	Total	18 / 20 Passed

Description

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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, specific description that names concrete capabilities across Astropy's major subsystems and provides an explicit 'Use when' trigger. Trigger-term coverage is excellent though not exhaustive of synonyms and file extensions.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete capabilities spanning the package's major subsystems ("units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology") plus concrete actions ("implementing or debugging astronomical data analysis code"), matching the comprehensive-coverage anchor.	5 / 5
Completeness	Explicitly answers both what ("Core Python library for astronomy and astrophysics workflows that need Astropy APIs, including...") and when ("Use when implementing or debugging astronomical data analysis code with Astropy") with concrete trigger phrasing.	5 / 5
Trigger Term Quality	Includes the natural domain terms users would say (Astropy, FITS I/O, coordinates, WCS, cosmology, units/quantities), but misses a few synonyms and file extensions (e.g., ".fits", "astronomy" as a standalone), so it falls just below the fully-comprehensive anchor.	4 / 5
Distinctiveness Conflict Risk	Targets a clear, domain-specific niche (the Astropy package and its astronomy APIs) with distinct triggers (FITS I/O, WCS, cosmology) that create minimal overlap risk with other skills.	5 / 5
	Total	19 / 20 Passed

Validation

100%

Warnings & errors only

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/astropy/SKILL.md
Commit: b2a92ba

Reviewed: 3 days ago

Table of Contents

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