Cantera Ignition Delay
K-Dense-AI/scientific-agent-skills
Runs Cantera constant-volume or constant-pressure ignition simulations and reports temperature-based ignition delay with mechanism provenance and checks.
Comprehensive Python library for astronomy and astrophysics.
$ npx skills add zLanqing/codex-claude-academic-skills --skill astropy -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install zLanqing/codex-claude-academic-skills astropy --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/zLanqing/codex-claude-academic-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/scientific-toolkit-skill/references/scientific-skills/astropy .claude/skills/astropy && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "astropy" agent skill from https://github.com/zLanqing/codex-claude-academic-skills/tree/main/scientific-toolkit-skill/references/scientific-skills/astropy into .claude/skills/astropy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "astropy", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/zLanqing/codex-claude-academic-skills/tree/main/scientific-toolkit-skill/references/scientific-skills/astropyType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add zLanqing/codex-claude-academic-skills --skill astropy -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install zLanqing/codex-claude-academic-skills astropy --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zLanqing/codex-claude-academic-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/scientific-toolkit-skill/references/scientific-skills/astropy .agents/skills/astropy && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "astropy" agent skill from https://github.com/zLanqing/codex-claude-academic-skills/tree/main/scientific-toolkit-skill/references/scientific-skills/astropy into .agents/skills/astropy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "astropy", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zLanqing/codex-claude-academic-skills --skill astropy -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install zLanqing/codex-claude-academic-skills astropy --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zLanqing/codex-claude-academic-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/scientific-toolkit-skill/references/scientific-skills/astropy .cursor/skills/astropy && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "astropy" agent skill from https://github.com/zLanqing/codex-claude-academic-skills/tree/main/scientific-toolkit-skill/references/scientific-skills/astropy into .cursor/skills/astropy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "astropy", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/zLanqing/codex-claude-academic-skills.git --path scientific-toolkit-skill/references/scientific-skills/astropy--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add zLanqing/codex-claude-academic-skills --skill astropy -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install zLanqing/codex-claude-academic-skills astropy --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zLanqing/codex-claude-academic-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/scientific-toolkit-skill/references/scientific-skills/astropy .gemini/skills/astropy && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "astropy" agent skill from https://github.com/zLanqing/codex-claude-academic-skills/tree/main/scientific-toolkit-skill/references/scientific-skills/astropy into .gemini/skills/astropy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "astropy", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install zLanqing/codex-claude-academic-skills astropyInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add zLanqing/codex-claude-academic-skills --skill astropy -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/zLanqing/codex-claude-academic-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/scientific-toolkit-skill/references/scientific-skills/astropy .github/skills/astropy && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "astropy" agent skill from https://github.com/zLanqing/codex-claude-academic-skills/tree/main/scientific-toolkit-skill/references/scientific-skills/astropy into .github/skills/astropy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "astropy", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add zLanqing/codex-claude-academic-skills --skill astropy -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install zLanqing/codex-claude-academic-skills astropy --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/zLanqing/codex-claude-academic-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/scientific-toolkit-skill/references/scientific-skills/astropy .opencode/skills/astropy && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "astropy" agent skill from https://github.com/zLanqing/codex-claude-academic-skills/tree/main/scientific-toolkit-skill/references/scientific-skills/astropy into .opencode/skills/astropy/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "astropy", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
astropyComprehensive Python library for astronomy and astrophysics.
Astropy is an agent skill from zLanqing/codex-claude-academic-skills. Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `references/coordinates.md`, `references/cosmology.md` and `references/fits.md`).
It sits in Research & Science, covering Physical and earth sciences. It works with Python. The repository describes itself as: 本仓库包含三个面向学术科研人员的Skills,覆盖从文献阅读、论文写作到科学计算的完整研究工作流。office-academic-skill 负责论文阅读报告与学术 PPT/Word 文档生成;research-writing-skill 提供论文写作、润色与审稿回复辅助;scientific-toolkit-skill 整合 MATLAB/Python… The licence is BSD-3-Clause.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 7ed6377. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
uvFrom the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
docs.astropy.orglearn.astropy.orggithub.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Astropy loads about 2.9k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 123 tokens; SKILL.md has 917 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from zLanqing/codex-claude-academic-skills at commit 7ed6377, republished under its BSD-3-Clause licence (© zLanqing). 917 words, ~2,884 tokens.
.claude/skills/astropy/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.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.
Use astropy when tasks involve:
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)astropy.units)Handle physical quantities with units, perform unit conversions, and ensure dimensional consistency in calculations.
Key operations:
.to() methodSee: references/units.md for comprehensive documentation, unit systems, equivalencies, performance optimization, and unit arithmetic.
astropy.coordinates)Represent celestial positions and transform between different coordinate frames.
Key operations:
SkyCoord in any frame (ICRS, Galactic, FK5, AltAz, etc.)See: references/coordinates.md for detailed coordinate frame descriptions, transformations, observer-dependent frames (AltAz), catalog matching, and performance tips.
astropy.cosmology)Perform cosmological calculations using standard cosmological models.
Key operations:
See: references/cosmology.md for available models, distance calculations, time calculations, density parameters, and neutrino effects.
astropy.io.fits)Read, write, and manipulate FITS (Flexible Image Transport System) files.
Key operations:
See: references/fits.md for comprehensive file operations, header manipulation, image and table handling, multi-extension files, and performance considerations.
astropy.table)Work with tabular data with support for units, metadata, and various file formats.
Key operations:
See: references/tables.md for table creation, I/O operations, data manipulation, sorting, filtering, joins, grouping, and performance tips.
astropy.time)Precise time representation and conversion between time scales and formats.
Key operations:
See: references/time.md for time formats, time scales, conversions, arithmetic, observing features, and precision handling.
astropy.wcs)Transform between pixel coordinates in images and world coordinates.
Key operations:
See: references/wcs_and_other_modules.md for WCS operations and transformations.
The references/wcs_and_other_modules.md file also covers:
Containers for n-dimensional datasets with metadata, uncertainty, masking, and WCS information.
Framework for creating and fitting mathematical models to astronomical data.
Tools for astronomical image display with appropriate stretching and scaling.
Physical and astronomical constants with proper units (speed of light, solar mass, Planck constant, etc.).
Image processing kernels for smoothing and filtering.
Robust statistical functions including sigma clipping and outlier rejection.
# Install astropy
uv pip install astropy
# With optional dependencies for full functionality
uv pip install astropy[all]from astropy.coordinates import SkyCoord
import astropy.units as u
# Create coordinate
c = SkyCoord(ra='05h23m34.5s', dec='-69d45m22s', frame='icrs')
# Transform to galactic
c_gal = c.galactic
print(f"l={c_gal.l.deg}, b={c_gal.b.deg}")
# Transform to alt-az (requires time and location)
from astropy.time import Time
from astropy.coordinates import EarthLocation, AltAz
observing_time = Time('2023-06-15 23:00:00')
observing_location = EarthLocation(lat=40*u.deg, lon=-120*u.deg)
aa_frame = AltAz(obstime=observing_time, location=observing_location)
c_altaz = c.transform_to(aa_frame)
print(f"Alt={c_altaz.alt.deg}, Az={c_altaz.az.deg}")from astropy.io import fits
import numpy as np
# Open FITS file
with fits.open('observation.fits') as hdul:
# Display structure
hdul.info()
# Get image data and header
data = hdul[1].data
header = hdul[1].header
# Access header values
exptime = header['EXPTIME']
filter_name = header['FILTER']
# Analyze data
mean = np.mean(data)
median = np.median(data)
print(f"Mean: {mean}, Median: {median}")from astropy.cosmology import Planck18
import astropy.units as u
import numpy as np
# Calculate distances at z=1.5
z = 1.5
d_L = Planck18.luminosity_distance(z)
d_A = Planck18.angular_diameter_distance(z)
print(f"Luminosity distance: {d_L}")
print(f"Angular diameter distance: {d_A}")
# Age of universe at that redshift
age = Planck18.age(z)
print(f"Age at z={z}: {age.to(u.Gyr)}")
# Lookback time
t_lookback = Planck18.lookback_time(z)
print(f"Lookback time: {t_lookback.to(u.Gyr)}")from astropy.table import Table
from astropy.coordinates import SkyCoord, match_coordinates_sky
import astropy.units as u
# Read catalogs
cat1 = Table.read('catalog1.fits')
cat2 = Table.read('catalog2.fits')
# Create coordinate objects
coords1 = SkyCoord(ra=cat1['RA']*u.degree, dec=cat1['DEC']*u.degree)
coords2 = SkyCoord(ra=cat2['RA']*u.degree, dec=cat2['DEC']*u.degree)
# Find matches
idx, sep, _ = coords1.match_to_catalog_sky(coords2)
# Filter by separation threshold
max_sep = 1 * u.arcsec
matches = sep < max_sep
# Create matched catalogs
cat1_matched = cat1[matches]
cat2_matched = cat2[idx[matches]]
print(f"Found {len(cat1_matched)} matches")For detailed information on specific modules:
references/units.md - Units, quantities, conversions, and equivalenciesreferences/coordinates.md - Coordinate systems, transformations, and catalog matchingreferences/cosmology.md - Cosmological models and calculationsreferences/fits.md - FITS file operations and manipulationreferences/tables.md - Table creation, I/O, and operationsreferences/time.md - Time formats, scales, and calculationsreferences/wcs_and_other_modules.md - WCS, NDData, modeling, visualization, constants, and utilities© zLanqing, BSD-3-Clause. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 7 other files (references) in scientific-toolkit-skill/references/scientific-skills/astropy of zLanqing/codex-claude-academic-skills.
Open the folder on GitHubat commit 7ed6377
We found 30 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 14 other GitHub owners. This page covers the copy in zLanqing/codex-claude-academic-skills, which our catalogue first saw on October 7, 2026.
Astropy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Astropy this skillzLanqing/codex-claude-academic-skills | 4.6k | 14 repos | ~2.9k | Automated safety check: Pass | BSD-3-Clause | |
| Cantera Ignition DelayK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Climate DsHongjian01/ClimWorkflow | 102 | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| Chemgraphargonne-lcf/ChemGraph | 162 | — | ~743 | Automated safety check: Pass | Apache-2.0 | |
| FluidSim CFD Simulationsdavila7/claude-code-templates | 32k | 10 repos | ~2.3k | Automated safety check: Pass | MIT | |
| DP-GEN Simplify Workflowjinzhezenggroup/computational-chemistry-agent-skills | 148 | — | ~2.7k | Automated safety check: Pass | LGPL-3.0-or-later |
K-Dense-AI/scientific-agent-skills
Runs Cantera constant-volume or constant-pressure ignition simulations and reports temperature-based ignition delay with mechanism provenance and checks.
Hongjian01/ClimWorkflow
ClimWorkflow climate-data workflow: map a natural-language climate goal to Plan-Agent / Data-Agent / Coding-Agent roles, then call the 7-tool DAG (optional read-only validate after report).
argonne-lcf/ChemGraph
Use ChemGraph Python and CLI workflows, agent-written batch scripts, and attached chemistry MCP tools.
davila7/claude-code-templates
Runs computational fluid dynamics simulations with the FluidSim Python framework: 2D and 3D Navier-Stokes, shallow water and stratified flow solvers plus output analysis.
jinzhezenggroup/computational-chemistry-agent-skills
Prepares, validates and runs DP-GEN simplify jobs that thin out repeated or redundant DeepMD datasets, generating param.json and machine.json for local or scheduler runs.
K-Dense-AI/scientific-agent-skills
Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API.
zLanqing/codex-claude-academic-skills
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Process-based discrete-event simulation framework in Python.
zLanqing/codex-claude-academic-skills
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python.
zLanqing/codex-claude-academic-skills
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zLanqing/codex-claude-academic-skills
Zero-shot time series forecasting with Google's TimesFM foundation model.
Works with
Categories
Comprehensive Python library for astronomy and astrophysics. Astropy is an agent skill from zLanqing/codex-claude-academic-skills. Comprehensive Python library for astronomy and astrophysics.
Astropy fits situations like: tasks involve coordinate transformations; unit conversions; FITS file manipulation; cosmological distance calculations.
Run `npx skills add zLanqing/codex-claude-academic-skills --skill astropy -a claude-code`. Or copy the skill folder (scientific-toolkit-skill/references/scientific-skills/astropy in zLanqing/codex-claude-academic-skills) into .claude/skills/astropy in your project. Claude Code loads it when a task matches its description.
Run `npx skills add zLanqing/codex-claude-academic-skills --skill astropy -a codex`. Or copy the skill folder (scientific-toolkit-skill/references/scientific-skills/astropy in zLanqing/codex-claude-academic-skills) into .agents/skills/astropy in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zLanqing/codex-claude-academic-skills --skill astropy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/astropy, .gemini/skills/astropy, .github/skills/astropy and .opencode/skills/astropy in your project.
Going by SKILL.md and its folder, Astropy needs the command-line tools its instructions call (uv). Our summary lists: Python 3.
SKILL.md names 3 domains. As links in the text: docs.astropy.org, learn.astropy.org and github.com. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Astropy is published under the BSD-3-Clause licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 13k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Astropy: Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars), Climate Ds (Hongjian01/ClimWorkflow, 102 stars), Chemgraph (argonne-lcf/ChemGraph, 162 stars) and FluidSim CFD Simulations (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
zLanqing (a GitHub user) maintains it in zLanqing/codex-claude-academic-skills, which has 4,578 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on May 14, 2026.
Source: zLanqing/codex-claude-academic-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.