astropy
by K-Dense-AIastropy is a Python toolkit for astronomy and astrophysics workflows. Use this astropy skill for celestial coordinates, units, FITS files, time scales, tables, WCS, cosmology, and astropy for Data Analysis. It helps with practical astronomy tasks like coordinate transforms, unit conversion, and data processing.
This skill scores 78/100, which means it is a solid listing candidate for Agent Skills Finder. Directory users should have enough context to install it with confidence for astronomy and astrophysics workflows, though they should expect some missing operational extras beyond the main SKILL.md guidance.
- Strong triggerability: the description explicitly covers coordinates, units, FITS, cosmology, time systems, tables, and WCS, making task matching straightforward.
- Good operational clarity: the body lays out concrete use cases such as ICRS/Galactic/FK5/AltAz conversions, time-scale handling, and FITS/table operations.
- Substantial workflow content: SKILL.md is long, structured, and free of placeholder markers, suggesting real guidance rather than a stub.
- No install command or supporting files are provided, so users may need to infer setup and dependency expectations themselves.
- The repository appears to rely on a single skill file with no scripts, references, or resources, which limits deeper implementation guidance and trust signals.
Overview of astropy skill
What astropy does
astropy is the Python toolkit for astronomy and astrophysics workflows. The astropy skill is a good fit when you need reliable handling of celestial coordinates, physical units, FITS data, time scales, tables, WCS, or cosmology calculations for astropy for Data Analysis.
Who should use it
Use this astropy guide if you are analyzing survey data, reducing observations, converting coordinates, or preparing astronomy notebooks and scripts. It is especially useful for researchers, data scientists, and engineers who need a practical astropy usage path rather than a generic Python answer.
What makes it different
The main value of astropy is consistency across astronomy-specific objects: Quantity, SkyCoord, Time, Table, and FITS/WCS tooling. That means fewer hand-rolled conversions and less risk of unit, frame, or time-scale mistakes.
How to Use astropy skill
Install astropy correctly
Install the skill with npx skills add K-Dense-AI/claude-scientific-skills --skill astropy. If you are deciding on astropy install, verify that your environment already has Python and the astronomy dependencies you expect to use, especially for FITS and coordinate-heavy tasks.
Give the skill a concrete astronomy task
The skill works best when your prompt includes the data type, target output, and any astronomy conventions. Good inputs specify things like coordinate frame, units, time scale, file format, or desired transformation. For example: “Convert RA/Dec from ICRS to AltAz for a given observatory and observation time” is stronger than “help with coordinates.”
Read the right files first
Start with SKILL.md, then inspect any linked repository guidance such as README.md, AGENTS.md, metadata.json, and supporting folders if present. For this repo, the core value is in the main skill file, so the fastest path is to read the overview, usage notes, and capability list before drafting your task.
Shape prompts for better output
Ask for the exact operation and the format you want back. Mention whether you need example code, a notebook cell, a calculation, or a debugging fix. If you have data, include a few representative column names, FITS headers, units, or a sample row so the astropy skill can produce code that matches your structure.
astropy skill FAQ
Is astropy only for professional astronomy work?
No. The astropy skill is also useful for student labs, pipeline scripts, and any Python workflow involving celestial data, units, or time handling. If your problem is astronomy-adjacent, astropy is usually a better fit than a generic prompt.
When should I not use astropy?
Skip it if your task is general data science without astronomy-specific concepts, or if you only need plain NumPy/Pandas logic. Also avoid it when the job is mostly visualization, because astropy is strongest in data modeling, conversion, and metadata-aware processing.
Is this better than asking for a Python script directly?
Usually yes, when the task depends on astronomy conventions. A generic Python prompt may miss frame definitions, unit conversion rules, or FITS/WCS details. The astropy skill helps constrain the response to the right scientific abstractions.
Is astropy beginner friendly?
Yes, if you can describe your scientific goal. Beginners get the best results when they state the input data, desired units, and expected output, instead of asking for a broad astropy usage explanation with no example.
How to Improve astropy skill
Provide the astronomy context that changes the answer
The biggest quality jump comes from naming the frame, unit system, time scale, and data source. For astropy for Data Analysis, include whether the work starts from FITS images, tables, catalog CSVs, or observation timestamps, because that changes the best code path.
Share a small representative sample
If the first answer is too generic, add a FITS header snippet, two or three table rows, or the exact coordinate strings you are using. This helps the astropy skill avoid assumptions about column names, sexagesimal parsing, or missing metadata.
Ask for the final deliverable you want
Say whether you need a reusable function, a notebook cell, validation checks, or a step-by-step explanation. If you want the answer to be production-ready, request unit checks, frame validation, and explicit error handling so the result is safer to run.
Iterate on the failure mode
If the result is close but not correct, say what broke: wrong frame, wrong units, wrong time scale, or wrong FITS extension. That feedback is more useful than asking for a “better version” because astropy problems usually fail in one specific astronomy convention.