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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.

70

Quality

86%

Does it follow best practices?

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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.

A well-organized, highly actionable reference skill with excellent progressive disclosure and verified bundle files. Its main weakness is conciseness — redundant capability lists and overlapping examples, plus time-sensitive dates outside a deprecated section — and the absence of validation feedback loops in its workflow recipes.

Suggestions

Trim the per-module "Key operations" bullet lists (which duplicate the reference files) to the few non-obvious operations, and consolidate the overlapping coordinate/FITS/cosmology examples between Quick Start and Common Workflows.

Move the specific release dates and version-research timestamps into a clearly labeled "Version notes / deprecations" section so time-sensitive information does not work against conciseness.

Add an explicit validation checkpoint to at least one multi-step workflow (e.g., verify catalog matches or check FITS HDU structure before analysis) to introduce a validate→fix→retry feedback loop.

DimensionReasoningScore

Conciseness

Mostly efficient and free of basic-concept filler, but the per-module "Key operations" bullet lists overlap with the reference files, Quick Start and Common Workflows repeat overlapping coordinate/FITS/cosmology examples, and time-sensitive dates ("released 2025-11-25; verified current as of 2026-06-10") sit outside a deprecated-patterns section, so it is not fully lean.

2 / 3

Actionability

Provides fully executable, import-complete Python (SkyCoord transforms, fits.open context managers, Planck18 distances, match_to_catalog_sky cross-matching) plus concrete install commands (uv pip install "astropy==7.2.0"), copy-paste ready.

3 / 3

Workflow Clarity

The body is well-structured (Overview → When to Use → Quick Start → Capabilities → Workflows → Best Practices) but the Common Workflows are sequential code recipes with no explicit validation checkpoints or validate→fix→retry feedback loops, so it stops short of the anchor for 3.

2 / 3

Progressive Disclosure

Each capability section closes with a clearly signaled one-level-deep "See: references/<file>.md" pointer, a dedicated Reference Files index lists all seven files with descriptions, and every referenced path (units, coordinates, cosmology, fits, tables, time, wcs_and_other_modules) exists in references/ — appropriately split and easy to navigate.

3 / 3

Total

10

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12

Passed

Description

100%

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 description: it names the domain and a comprehensive set of concrete Astropy subsystems, provides a third-person voice with no first/second-person slips, and includes an explicit "Use when..." trigger. No significant gaps.

DimensionReasoningScore

Specificity

Names multiple concrete subsystems — "units/quantities, coordinates, FITS I/O, tables, time systems, WCS, and cosmology" — giving a comprehensive, specific capability list rather than vague language.

3 / 3

Completeness

Explicitly answers both what ("Core Python library ... including [subsystems]") and when ("Use when implementing or debugging astronomical data analysis code with Astropy."), with an explicit trigger clause.

3 / 3

Trigger Term Quality

Covers natural terms users would say — "Astropy", "astronomy and astrophysics", "FITS", "coordinates", "WCS", "cosmology", "astronomical data analysis" — across common variations.

3 / 3

Distinctiveness Conflict Risk

Scoped to a named library (Astropy) with astronomy-specific triggers, giving it a clear niche unlikely to conflict with other skills.

3 / 3

Total

12

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12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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