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astropy

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

48

Quality

53%

Does it follow best practices?

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SecuritybySnyk

Passed

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

Quality

Content

65%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 actionable with strong executable examples and a clean reference structure, but it is held back by redundant capability lists, workflow recipes lacking validation checkpoints, and reference files that are placeholders rather than real detailed content.

Suggestions

Replace the placeholder reference files with actual module detail, or remove the per-module 'See' links until the content exists, so progressive disclosure does not dead-end.

Collapse the overlapping 'When to Use This Skill' and 'Core Capabilities' bullet lists into a single concise capability map to tighten conciseness.

Add explicit validation checkpoints to the write-oriented workflows (e.g., verify a FITS file round-trips with fits.open before treating it as complete).

DimensionReasoningScore

Conciseness

The body is mostly efficient lists and code rather than padded prose, but the 'When to Use This Skill' list and the per-module 'Key operations' bullet lists overlap substantially and could be consolidated.

3 / 5

Actionability

The Quick Start and Common Workflows sections provide fully executable, copy-paste-ready Python covering coordinate conversion, FITS reading, cosmology, and catalog cross-matching across the common cases.

5 / 5

Workflow Clarity

Workflows are presented as monolithic code recipes with no explicit sequencing or validation checkpoints; for operations like FITS writing there is no validate-then-proceed feedback loop, so checkpoints are only implicit.

3 / 5

Progressive Disclosure

Structure and navigation are good (clear 'See: references/X.md' links, one level deep, a Reference Files index, and the referenced files exist), but every reference file is an empty placeholder that redirects upstream, so the promised detailed content is absent and disclosure dead-ends.

3 / 5

Total

14

/

20

Passed

Description

41%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 identifies a clear niche (astronomy) but is too generic: it states no concrete capabilities, lacks a 'Use when...' trigger clause, and relies on vague phrasing rather than the specific terms users would naturally say.

Suggestions

List 3-4 concrete capabilities in the description (e.g., 'Convert celestial coordinates, read/write FITS files, perform unit-aware calculations and cosmological distance computations').

Add an explicit 'Use when...' clause naming natural triggers such as FITS files, coordinate transformations, physical units, cosmology, or WCS.

Replace 'essential functionality for astronomical research and data analysis' with specific, user-voiced keywords to raise trigger term quality.

DimensionReasoningScore

Specificity

The description names the domain ('core Python package for astronomy') but the only action offered is the generic phrase 'providing essential functionality for astronomical research and data analysis', with no concrete operations like coordinate conversion or FITS handling.

2 / 5

Completeness

It gives a clear 'what' (core astronomy package providing essential functionality) but has no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric.

3 / 5

Trigger Term Quality

It surfaces 'astronomy'/'astronomical' but omits the natural specific terms users actually say when they need astropy (FITS, coordinates, units, cosmology, WCS), leaving only one or two generic keywords.

2 / 5

Distinctiveness Conflict Risk

The astronomy niche is clear and distinct from non-astronomy skills, though the generic 'astronomical research and data analysis' phrasing leaves minor overlap risk with closely related science packages.

4 / 5

Total

11

/

20

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/agentic-awesome-skills
Reviewed

Table of Contents

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