astropy

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

Quality

61%

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Quality

Content

75%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.

The body is a well-structured, highly actionable reference: executable Quick Start and workflow examples cover every major module, and navigation to per-module reference files is clearly signaled. Its weaknesses are systematic redundancy (reference files listed twice, Overview restating the description) and reference files that are placeholders pointing to upstream docs rather than containing the promised detail.

Suggestions

Remove the duplicated '## Reference Files' section (or the per-module 'See:' lines) — one clear index of references is enough, and consolidate 'Additional Capabilities' into it.

Populate the references/*.md files with actual content instead of placeholder redirects, or reword the 'See:' promises (e.g. 'for the full API, see the upstream astropy docs') so the pointer is honest about being one level deep.

Tighten the Overview/When-to-Use sections, which restate the frontmatter description and repeat the module list that the Core Capabilities sections already cover.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient (no explaining concepts Claude already knows, tight code examples), but it repeats itself: the Overview paragraph restates the frontmatter description verbatim, the reference files are listed both in per-module 'See:' lines and again in a full '## Reference Files' section, and 'Additional Capabilities' overlaps that same list. This matches 'mostly efficient but includes some unnecessary explanation or could be tightened', not 4, because the duplication is systematic rather than a minor instance.	3 / 5
Actionability	The Quick Start and all four Common Workflows (coordinate conversion, FITS analysis, cosmological distances, catalog cross-matching) are complete, executable, copy-paste-ready Python, plus concrete install commands. This matches the top anchor: specific examples cover the common cases for each major module.	5 / 5
Workflow Clarity	Each workflow is a coherent, unambiguous script and there are no destructive or batch operations that would demand validation checkpoints. It is not 5 because the skill spans seven modules with multi-step real-world tasks (e.g. cross-matching, AltAz transforms requiring time+location) presented only as flat scripts, with no guidance on sequencing or error handling across steps.	4 / 5
Progressive Disclosure	Structure is good: per-module sections with well-signaled one-level-deep 'See: references/<file>.md' pointers, and all 7 referenced files exist with matching names. It is not 5 because the referenced files are placeholders that redirect to the upstream astropy docs, so the promised 'comprehensive documentation' is actually a second hop away — the split exists in form but not in substance.	4 / 5
	Total	16 / 20 Passed

Description

48%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.

The description establishes a clear, distinctive domain (astronomy in Python) but stops there: it lists no concrete capabilities and provides no 'Use when...' trigger guidance. It reads more like a package tagline than an actionable skill description.

Suggestions

Add an explicit trigger clause, e.g. 'Use when working with FITS files, celestial coordinates, astronomical units and quantities, cosmological calculations, or astronomical time/tables.'

Replace 'providing essential functionality for astronomical research and data analysis' with 3-5 concrete actions such as 'convert between celestial coordinate systems, perform unit-aware calculations, read and write FITS files, and compute cosmological distances.'

Include natural trigger keywords users would actually say (FITS, .fits, WCS, SkyCoord, Julian Date, MJD, cross-matching catalogs) so the skill fires on the right requests.

Dimension	Reasoning	Score
Specificity	The description names the domain ('core Python package for astronomy') but the only capability statement is the generic phrase 'providing essential functionality for astronomical research and data analysis' — no concrete actions like coordinate transformations, FITS handling, or unit conversion are listed. It matches anchor 2 (names domain, minimal/generic actions), not 3, because it does not name even 1-2 concrete actions.	2 / 5
Completeness	It has a clear 'what' ('core Python package for astronomy... essential functionality') but no 'when' clause at all — there is no 'Use when...' or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines. It is not 4 because the 'when' is entirely absent rather than just under-specified.	3 / 5
Trigger Term Quality	Natural terms present are 'astronomy', 'astronomical research', and 'data analysis', which a user would plausibly say; but common variations users actually use — FITS files, celestial coordinates, units/quantities, cosmology, catalogs — are missing. This matches anchor 3 (some relevant keywords, missing common variations), not 4, because coverage is thin for such a broad package.	3 / 5
Distinctiveness Conflict Risk	'The core Python package for astronomy' carves out a clear niche with low overlap risk against non-astronomy skills, matching 'mostly distinct; minor overlap risk'. It is not 5 because the thin trigger phrasing ('essential functionality', 'data analysis') is generic enough that it could overlap with general data-analysis or visualization skills.	4 / 5
	Total	12 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: sickn33/agentic-awesome-skills
Path: skills/astropy/SKILL.md
Commit: 7b534bc

Reviewed: 6 days ago

Table of Contents

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