astropy

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

Quality

62%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/astropy/SKILL.md

Quality

Content

76%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 library reference with executable examples and clean progressive-disclosure signaling, held back only by missing validation checkpoints in batch workflows and referenced detail files that are not actually bundled.

Suggestions

Add a validation step to the cross-matching and FITS-writing workflows (e.g. verify match counts or re-open written files) to lift workflow clarity above the destructive/batch cap of 3.

Either bundle the seven referenced references/*.md files or remove the 'See: references/X.md' pointers so signaled navigation does not dead-end.

Dimension	Reasoning	Score
Conciseness	The body is dense and largely content-bearing with no padding about concepts Claude already knows; the only minor trim is the Overview restating the frontmatter description and some overlap between the 'When to Use' list and per-module 'Key operations', so it sits at 'efficient; minor instances that could be trimmed' rather than the lean-every-token-earns-its-place 5 anchor.	4 / 5
Actionability	It provides copy-paste-ready, executable code across Quick Start and four Common Workflows (coordinate transforms, FITS reading, cosmological distances, catalog cross-matching) using correct astropy APIs, matching 'fully executable; copy-paste ready; covers the common cases'.	5 / 5
Workflow Clarity	Workflows are clear code recipes but contain no validation/verification checkpoints, and catalog cross-matching and FITS writing are batch/destructive-style operations — the guideline caps workflow clarity at 3 in that case, so it cannot reach 4 despite the clean sequencing.	3 / 5
Progressive Disclosure	Structure is excellent — overview, a 'See: references/X.md' pointer per module, and a consolidated Reference Files list — but the seven referenced reference files do not exist in the bundle (no references/ directory), so navigation leads to dead ends, fitting 'good structure; minor organization gaps' rather than the fully-realized 5 anchor.	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 correctly identifies a distinct niche (Astropy for astronomy) but is generic about what it does and omits any 'Use when' trigger guidance, capping completeness at 3 and specificity at 2.

Suggestions

Add 3-4 concrete capabilities, e.g. 'coordinate transformations, unit/quantity calculations, FITS I/O, cosmological distances, and WCS'.

Append an explicit trigger clause: 'Use when working with FITS files, celestial coordinates, astronomical units, cosmology, or WCS transforms.'

Include natural synonyms/file extensions users say (FITS, .fits, celestial coordinates, redshift) to lift trigger-term coverage.

Dimension	Reasoning	Score
Specificity	It names the domain ('core Python package for astronomy') but the only action offered is the generic 'providing essential functionality for astronomical research and data analysis' — no concrete capabilities are listed, matching the 'names domain but actions minimal/generic' anchor rather than the 3 anchor which requires 1-2 concrete actions.	2 / 5
Completeness	There is a clear 'what' (core Python package for astronomy) but no 'when'/Use-when clause at all; per the judging guidelines a missing explicit trigger clause caps completeness at 3, and it does not reach 4 which requires both what and when.	3 / 5
Trigger Term Quality	Relevant natural terms are present ('astronomy', 'astronomical research', 'data analysis', 'Astropy') but common variations users actually say — FITS, celestial coordinates, units/quantities, cosmology — are absent, fitting 'some relevant keywords but missing common variations' rather than the fuller 4 anchor.	3 / 5
Distinctiveness Conflict Risk	The named Astropy/astronomy niche is mostly distinct with only minor overlap risk against other astro-related skills; it stops short of 5 because there is no explicit trigger phrase and 'essential functionality' is broad within astronomy.	4 / 5
	Total	12 / 20 Passed

Validation

81%

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 — 13 / 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
referenced_paths_exist	Referenced path issues: 15 missing	Warning

	Total	13 / 16 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: plugins/AI-Agents-Safe-Coding-Skills/skills/astropy/SKILL.md
Commit: debb2ec

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.