astropy
Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.
37
36%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/astropy/SKILL.mdQuality
Discovery
22%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This description reads more like a tagline from the Astropy project homepage than a functional skill description. It lacks concrete actions, specific trigger terms, and any 'Use when...' guidance, making it very difficult for Claude to know when to select this skill over others. The domain is identifiable but the description provides almost no actionable detail.
Suggestions
Add a 'Use when...' clause with explicit triggers, e.g., 'Use when the user asks about astronomical calculations, FITS files, celestial coordinates, WCS transformations, or astropy-related Python code.'
List specific concrete actions such as 'Handles FITS file I/O, celestial coordinate transformations, unit conversions, cosmological calculations, and astronomical table operations.'
Include natural user terms and file extensions like 'FITS', '.fits', 'sky coordinates', 'RA/Dec', 'astropy units', 'cosmology calculations' to improve trigger term coverage.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description only names the domain ('astronomy') and uses vague language like 'essential functionality' and 'data analysis' without listing any concrete actions such as coordinate transformations, FITS file handling, unit conversions, or table operations. | 1 / 3 |
Completeness | The description weakly addresses 'what' (essential functionality for astronomy) but is entirely missing a 'when' clause or any explicit trigger guidance for when Claude should select this skill. | 1 / 3 |
Trigger Term Quality | It includes some relevant keywords like 'Astropy', 'Python', 'astronomy', and 'astronomical research', but misses common user terms like 'FITS files', 'celestial coordinates', 'WCS', 'astropy units', 'sky catalog', or specific astronomical data formats. | 2 / 3 |
Distinctiveness Conflict Risk | The mention of 'Astropy' and 'astronomy' provides some distinctiveness, but 'data analysis' and 'Python package' are generic enough to overlap with general data science or Python skills. | 2 / 3 |
Total | 6 / 12 Passed |
Implementation
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill provides excellent actionable code examples that are executable and cover the major astropy use cases well. However, it is significantly too verbose — it over-explains what each module does with extensive bullet lists before pointing to reference files, essentially creating a redundant layer of documentation. The content would benefit greatly from trimming the 'Core Capabilities' section to brief one-liners with references, removing the 'When to Use' section (which duplicates the overview), and cutting the external documentation links.
Suggestions
Drastically reduce the 'Core Capabilities' section: replace the bullet-point lists of key operations with 1-2 sentence summaries per module, since the reference files already contain this detail.
Remove or merge the 'When to Use This Skill' section into the Overview — it largely repeats the same information and adds ~10 lines of redundant content.
Remove the 'Documentation and Resources' section with external URLs — Claude cannot browse these links, and they waste tokens.
Add validation/error-handling guidance to the common workflows (e.g., checking if a FITS file opened successfully, verifying WCS validity before transformations, handling empty cross-match results).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~250+ lines. It explains what astropy is, lists every module's capabilities in bullet-point form (information Claude already knows), repeats the 'When to Use' section content in the 'Core Capabilities' section, includes external documentation links Claude can't access, and provides a 'Best Practices' list of 10 items that are mostly common sense for an LLM familiar with Python astronomy libraries. | 1 / 3 |
Actionability | The skill provides multiple fully executable, copy-paste ready code examples covering coordinate transformations, FITS file handling, cosmological calculations, and catalog cross-matching. All code blocks use real imports and concrete values. | 3 / 3 |
Workflow Clarity | The common workflows section provides clear step-by-step code examples, but there are no validation checkpoints or error handling steps. For operations like FITS file manipulation or catalog cross-matching, there's no guidance on what to do when things fail or how to verify results beyond printing values. | 2 / 3 |
Progressive Disclosure | The skill references 7 separate reference files with clear paths, which is good structure. However, the main SKILL.md itself contains too much inline content that duplicates what the reference files presumably cover — each core capability section lists all key operations before pointing to the reference file, making the overview bloated rather than concise. | 2 / 3 |
Total | 8 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 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 |
Total | 9 / 11 Passed | |
- Repository
- sickn33/antigravity-awesome-skills
- Commit
4cc48db
- Reviewed
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