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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.

71

Quality

89%

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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 strong library skill body: executable, honestly-caveated code examples; a clean overview-to-reference-file architecture with verified one-level-deep navigation; and version-specific details correctly quarantined in a dedicated migration notes section. Remaining polish is tightening the redundancy between the description, the When-to-Use section, and per-module bullet lists, and adding a general validation/feedback-loop pattern for risky write operations.

DimensionReasoningScore

Conciseness

The body is efficient overall: a compact Quick Start covering all seven sub-systems, lean code blocks, and no padding explaining concepts Claude already knows (it never explains what FITS or a parsec is). Minor trimmable instances remain — the "When to Use This Skill" section restates the frontmatter description, and some "Key operations" bullets (e.g., "Create quantities by multiplying values with units") state operations inferable from the API — keeping it below the score-5 anchor's "every token earns its place".

4 / 5

Actionability

Guidance is fully executable: a copy-paste Quick Start importing all core modules, four complete workflow scripts (coordinate transforms, FITS analysis, cosmological distances, catalog cross-matching), and exact pinned install commands ("uv pip install \"astropy==8.0.1\""). Specific examples cover the common cases, and file-based blocks are honestly flagged as "illustrative and require those inputs" — matching the score-5 anchor.

5 / 5

Workflow Clarity

The navigation sequence (When to Use → Quick Start → per-module capabilities with See-links → worked workflows) is clear, and several workflows include explicit pre-flight checkpoints ("Confirm both catalogs use ICRS and compatible reference epochs before proceeding"; "verify a pixel → world → pixel round trip before extracting sources"). It falls short of the score-5 anchor because there is no general error-recovery/feedback-loop guidance (validate → fix → retry) for the riskier write/fitting operations, fitting the score-4 anchor (clear sequence with most checkpoints, minor gaps).

4 / 5

Progressive Disclosure

SKILL.md is a genuine overview that routes each capability to a well-signaled, one-level-deep reference file ("See: references/units.md for comprehensive documentation...") — all 8 referenced files exist in references/ and link only to external documentation, not to each other. A closing "Reference Files" section indexes every file with a one-line description, matching the score-5 anchor's clear overview with appropriately split content and easy navigation.

5 / 5

Total

18

/

20

Passed

Description

87%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 well-crafted description with explicit what-and-when structure, domain-specific trigger terms, and near-comprehensive capability coverage. The only refinement available is converting noun-domain phrases into action verbs and adding a few common synonyms/extensions (e.g., ".fits", "redshift").

DimensionReasoningScore

Specificity

The description enumerates seven concrete capability areas — "units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology" — giving comprehensive coverage of the library's scope, but these are noun-domain phrases rather than the explicit action verbs of the score-5 anchor (e.g., "FITS I/O" implies read/write without stating it). It clearly exceeds the score-3 anchor ("1-2 concrete actions") on breadth and specificity.

4 / 5

Completeness

Both parts are explicit: the "what" is a clear capability enumeration ("Core Python library... including units/quantities, coordinates, FITS I/O...") and the "when" is a concrete trigger clause — "Use when implementing or debugging astronomical data analysis code with Astropy" — matching the score-5 anchor's pattern of what + when with concrete trigger phrases.

5 / 5

Trigger Term Quality

Strong natural keyword coverage — "astronomy", "astrophysics", "Astropy", "FITS", "coordinates", "cosmology", "tables", "WCS" — matching phrases a user would actually say. It falls short of the score-5 anchor's comprehensive synonym/extension coverage because common variations like ".fits", "redshift", "celestial coordinates", or "catalog" are absent.

4 / 5

Distinctiveness Conflict Risk

"Astropy" and "astronomy and astrophysics" carve out a clear niche, and terms like "FITS I/O", "WCS", and "cosmology" are domain-specific with minimal overlap risk against generic skills. The generic word "tables" carries slight overlap risk with general tabular-data skills, but the astronomy framing dominates, fitting the score-5 anchor (clear niche with distinct triggers).

5 / 5

Total

18

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/astropy/SKILL.md

Reviewed

Table of Contents

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