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astropy

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

56

Quality

64%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Fix and improve this skill with Tessl

tessl review fix ./skills/astropy/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%

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 strong, actionable capability reference with executable examples and a well-structured one-level-deep reference layout. Its main weaknesses are minor redundancy and the absence of explicit validation checkpoints in the workflows.

Suggestions

Remove the standalone 'Reference Files' section (or the inline 'See:' lines) to eliminate duplicated reference pointers and tighten token usage.

Embed validation checkpoints into the multi-step workflows, e.g. after WCS or coordinate transforms add 'Verify the output frame/units before proceeding' as an explicit step.

Populate the placeholder reference files with actual module detail rather than stubs pointing to upstream docs, so the disclosed references deliver on the inline promises.

DimensionReasoningScore

Conciseness

The body is mostly efficient API guidance and executable code, but the bottom 'Reference Files' section redundantly repeats the inline 'See: references/X.md' pointers already given in each Core Capability section, and 'Additional Capabilities' restates items covered by the wcs reference.

2 / 3

Actionability

Provides a Quick Start plus four complete, copy-paste-ready Python workflows (coordinate transforms, FITS analysis, cosmological distances, catalog cross-matching) using concrete real methods like SkyCoord, match_to_catalog_sky, and Planck18.luminosity_distance.

3 / 3

Workflow Clarity

Common Workflows present clear linear sequences, but none include explicit validation checkpoints or fix-and-retry feedback loops; verification hints appear only as disconnected Best Practices items rather than embedded workflow steps.

2 / 3

Progressive Disclosure

The SKILL.md is an overview that cleanly splits each module into its own one-level-deep, clearly-signaled reference file (references/units.md, coordinates.md, etc.), all referenced paths exist as real bundle files, and navigation is duplicated inline plus in a Reference Files list.

3 / 3

Total

10

/

12

Passed

Description

57%

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 astronomy niche but stays vague about concrete capabilities and omits any 'Use when' trigger guidance. It is competent but generic where it should be specific.

Suggestions

Replace 'providing essential functionality' with specific concrete actions, e.g. 'convert celestial coordinates, handle FITS files, compute cosmological distances, and manage astronomical units'.

Add an explicit trigger clause such as 'Use when working with FITS files, celestial coordinates, astronomical units, cosmological calculations, or WCS transformations'.

Surface the natural trigger terms (FITS, coordinates, units, WCS, redshift, parsec) that users would actually say when they need this skill.

DimensionReasoningScore

Specificity

Names a concrete domain ('core Python package for astronomy') but the actions are abstract ('providing essential functionality for astronomical research and data analysis') rather than listing specific concrete operations like coordinate transforms or FITS handling.

2 / 3

Completeness

Clearly answers 'what' (core astronomy Python package for research/data analysis) but has no 'Use when...' clause or equivalent explicit trigger guidance, so completeness is capped at 2.

2 / 3

Trigger Term Quality

Includes relevant natural terms ('astronomy', 'astronomical research', 'data analysis') but misses the common variations a user would actually say for this skill (FITS, coordinates, units, WCS, redshift).

2 / 3

Distinctiveness Conflict Risk

The astronomy-specific niche is clearly stated and unlikely to trigger for unrelated skills; conflict risk with other skills is low.

3 / 3

Total

9

/

12

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

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/antigravity-awesome-skills
Reviewed

Table of Contents

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