Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentlas-ai/agentlas-desktop --skill astropygit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/astropy)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/astropy"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/astropy/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/astropy"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/astropy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.03751 |
| Opus 5.5 | $0.00022 | $0.01500 |
| Sonnet 5.5 | $0.00011 | $0.00750 |
| Haiku 4.5 | $0.00006 | $0.00375 |
Grade A, and why
astropy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to astropy — 60 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Astropy
Overview
Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis. Use astropy for coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing.
When to Use This Skill
Use astropy when tasks involve:
- Converting between celestial coordinate systems (ICRS, Galactic, FK5, AltAz, etc.)
- Working with physical units and quantities (converting Jy to mJy, parsecs to km, etc.)
- Reading, writing, or manipulating FITS files (images or tables)
- Cosmological calculations (luminosity distance, lookback time, Hubble parameter)
- Precise time handling with different time scales (UTC, TAI, TT, TDB) and formats (JD, MJD, ISO)
- Table operations (reading catalogs, cross-matching, filtering, joining)
- WCS transformations between pixel and world coordinates
- Astronomical constants and calculations
Quick Start
import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.time import Time
from astropy.io import fits
from astropy.table import Table
from astropy.cosmology import Planck18
# Units and quantities
distance = 100 * u.pc
distance_km = distance.to(u.km)
# Coordinates
coord = SkyCoord(ra=10.5*u.degree, dec=41.2*u.degree, frame='icrs')
coord_galactic = coord.galactic
# Time
t = Time('2023-01-15 12:30:00')
jd = t.jd # Julian Date
# FITS files
data = fits.getdata('image.fits')
header = fits.getheader('image.fits')
# Tables
table = Table.read('catalog.fits')
# Cosmology
d_L = Planck18.luminosity_distance(z=1.0)
Core Capabilities
1. Units and Quantities (astropy.units)
Handle physical quantities with units, perform unit conversions, and ensure dimensional consistency in calculations.
Key operations:
- Create quantities by multiplying values with units
- Convert between units using
.to()method - Perform arithmetic with automatic unit handling
- Use equivalencies for domain-specific conversions (spectral, doppler, parallax)
- Work with logarithmic units (magnitudes, decibels)
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 370 lines · 56 tokens per session scan A f2cc909204ff
astropy is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 3,751 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to astropy, differing in 60 lines, and is treated as a copy.
Other skills, from other repositories
tbtools
Use when the user asks about TBtools, TBtools-II, TBtools RPC API, TBtools CLI, or bioinformatics operations available through TBtools such as sequence manipulation, BLAST, GFF/GTF/GXF processing, expression tables, heatmaps, trees, MCScanX, DIAMOND, HMMER, MUSCLE, and IQ-TREE.
electron-expert
Expert in Electron framework, desktop app development, IPC, and cross-platform packaging. Use when the user mentions desktop, Node.js, cross platform, Windows, macOS, or Linux, or when the task involves Electron Architecture, Process Types, IPC Communication, or App Lifecycle.
autoresearch
Run an auto research / autoresearch loop for measurable improvements. Use when optimizing a skill, agent, prompt, code path, benchmark, or other target with a mechanical metric: baseline, mutate one thing, verify, keep or discard, log, chart progress, and optionally save improved custom skills or agents through Open…
research-orchestrator
Becomes a principal research scientist who orchestrates deep, multi-source research by coordinating evidence gathering, grading source quality, and synthesizing findings with explicit confidence levels. Use when the user needs a comprehensive research report on a complex topic, evidence-based analysis with source…
architecture-diagram
How to draw and maintain the architecture diagram Ensemblr keeps for a workspace — ensemblrgetarchitecturediagram to fetch the stored one (it answers with null when nobody has drawn this workspace yet), the IR shape, how to derive it from the codebase, and ensemblrupdatearchitecturediagram to store it. Read this when…
scaffold-lean-library
Scaffold a Lean 4 library with Mathlib or PFR, Lake tests, and CI. Use for "new Lean project"; to edit Lean, use write-lean-code.