CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

astropy

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

Quality

61%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

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

Quality

Content

53%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-organized, honest overview with genuinely executable workflow examples and clear one-level-deep reference signaling, but it over-relies on descriptive capability bullets that restate what Claude already knows about astropy, and its reference layer is hollow — seven placeholder files that redirect to upstream docs, creating nested indirection the skill only partially mitigates by disclosing it.

Suggestions

Replace the per-module 'Key operations' bullet lists (which restate astropy's API, something Claude largely knows) with one concrete code snippet per module showing the non-obvious idiom, cutting the body substantially.

Fill the references/*.md files with the promised content (equivalencies tables, AltAz recipe, header-editing patterns) or collapse them into a single 'upstream docs' pointer file to remove the placeholder indirection layer.

Move the IERS/offline/cross-matching caveats out of the Installation section into a dedicated 'Caveats' section with a short checklist form, and add an explicit validation step to the cross-matching workflow.

DimensionReasoningScore

Conciseness

The body is mostly efficient — terse bullet lists, no 'what is astronomy' padding, executable examples without narration — but it could be tightened: the 'Reference Files' section repeats the seven 'See:' pointers already given in 'Core Capabilities', the Overview restates the description, and the 'Key operations' bullets largely enumerate library features Claude already knows about astropy. The dense 6-line paragraph about IERS/auto-download/cross-matching caveats (line 200) is shoehorned into the Installation section and reads as an unstructured wall of text.

3 / 5

Actionability

Mostly executable guidance: the Quick Start plus four Common Workflows (coordinate transform to AltAz, FITS open/header access, cosmological distances, catalog cross-match) are copy-paste ready and cover the common cases, and install commands are given. It falls short of 5 because much of the body is descriptive capability bullets ('Read and modify headers', 'Create custom WCS objects') without the concrete calls for those cases, and no example shows writing output files.

4 / 5

Workflow Clarity

Workflows are presented as worked code examples rather than sequenced steps with checkpoints, and validation is implicit or absent (e.g., the cross-matching workflow filters by separation but has no verify step; the caveat paragraph at line 200 mentions inspecting epochs/units/matching policy but is buried and unstructured). It is above a 2 because the workflows themselves are coherent and Best Practices includes checks like 'Check coordinate frames' and 'Check WCS validity'.

3 / 5

Progressive Disclosure

Judged against the actual bundle: all seven referenced files exist, but each is a placeholder that only links to upstream astropy docs, so the effective chain is SKILL.md → references/*.md → external URLs — two levels of indirection to the real content, matching the anti-pattern 'See advanced.md for details. (In advanced.md: See details.md for the actual instructions.)'. The body honestly discloses this at line 326 and the inline content is well-signaled, which keeps it above a 1, but content that clearly should be bundled (the promised 'comprehensive documentation') is missing.

2 / 5

Total

12

/

20

Passed

Description

52%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 the package and its niche clearly but says almost nothing about what it concretely does or when to use it. 'Essential functionality' is vague filler, there is no 'Use when...' trigger clause, and specific module keywords (FITS, coordinates, units, time) that users naturally say are absent.

Suggestions

Enumerate concrete capabilities in the description itself, e.g. 'Transform celestial coordinates, convert physical units, read/write FITS files, compute cosmological distances, handle precise time scales and astronomical tables.'

Add an explicit trigger clause such as 'Use when working with FITS files, sky coordinates, astronomical units/quantities, redshift/cosmology calculations, or observatory time data.'

Include natural keywords and synonyms users actually say (FITS, .fits, WCS, catalog cross-matching, Julian Date/MJD, redshift) rather than the generic phrase 'essential functionality'.

DimensionReasoningScore

Specificity

The description names the domain ('core Python package for astronomy') but the only action phrase is 'providing essential functionality for astronomical research and data analysis', which is pure abstract language with no concrete capability named (no coordinate transforms, FITS, units, cosmology, time). This matches the anchor 'Names the domain but actions are minimal or generic' ('Processes PDF files'); it is above score 1 only because the domain and package are clearly identified.

2 / 5

Completeness

The 'what' is present but only at the package/domain level ('core Python package for astronomy'); no concrete functionality is enumerated, and there is no 'Use when...' clause or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines. It is not a 2 because the package identity and domain are stated clearly rather than vaguely.

3 / 5

Trigger Term Quality

Relevant keywords present are 'Astropy', 'Python', 'astronomy', 'astronomical research and data analysis' — comparable keyword density to the anchor example 'Works with PDF files'. Common natural terms users would say are missing: FITS/.fits files, coordinates, units, redshift, WCS, catalogs, time scales — so coverage is partial rather than good.

3 / 5

Distinctiveness Conflict Risk

'Astropy is the core Python package for astronomy' carves out a clear niche (astronomy) with distinct trigger terms ('astropy', 'astronomy', 'astronomical research and data analysis') and minimal overlap risk with other Python/scientific skills; a user mentioning this domain would not be routed to the wrong skill.

5 / 5

Total

13

/

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.

Validation — 14 / 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

Path

skills/astropy/SKILL.md

Reviewed

Table of Contents

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.