Agent skill
skill-installer
Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos).
Install this agent skill to your Project
npx add-skill https://github.com/x-cmd/skill/tree/main/data/openai/.system/skill-installer
Metadata
Additional technical details for this skill
- short description
- Install curated skills from openai/skills or other repos
SKILL.md
Skill Installer
Helps install skills. By default these are from https://github.com/openai/skills/tree/main/skills/.curated, but users can also provide other locations. Experimental skills live in https://github.com/openai/skills/tree/main/skills/.experimental and can be installed the same way.
Use the helper scripts based on the task:
- List skills when the user asks what is available, or if the user uses this skill without specifying what to do. Default listing is
.curated, but you can pass--path skills/.experimentalwhen they ask about experimental skills. - Install from the curated list when the user provides a skill name.
- Install from another repo when the user provides a GitHub repo/path (including private repos).
Install skills with the helper scripts.
Communication
When listing skills, output approximately as follows, depending on the context of the user's request. If they ask about experimental skills, list from .experimental instead of .curated and label the source accordingly:
"""
Skills from {repo}:
- skill-1
- skill-2 (already installed)
- ... Which ones would you like installed? """
After installing a skill, tell the user: "Restart Codex to pick up new skills."
Scripts
All of these scripts use network, so when running in the sandbox, request escalation when running them.
scripts/list-skills.py(prints skills list with installed annotations)scripts/list-skills.py --format json- Example (experimental list):
scripts/list-skills.py --path skills/.experimental scripts/install-skill-from-github.py --repo <owner>/<repo> --path <path/to/skill> [<path/to/skill> ...]scripts/install-skill-from-github.py --url https://github.com/<owner>/<repo>/tree/<ref>/<path>- Example (experimental skill):
scripts/install-skill-from-github.py --repo openai/skills --path skills/.experimental/<skill-name>
Behavior and Options
- Defaults to direct download for public GitHub repos.
- If download fails with auth/permission errors, falls back to git sparse checkout.
- Aborts if the destination skill directory already exists.
- Installs into
$CODEX_HOME/skills/<skill-name>(defaults to~/.codex/skills). - Multiple
--pathvalues install multiple skills in one run, each named from the path basename unless--nameis supplied. - Options:
--ref <ref>(defaultmain),--dest <path>,--method auto|download|git.
Notes
- Curated listing is fetched from
https://github.com/openai/skills/tree/main/skills/.curatedvia the GitHub API. If it is unavailable, explain the error and exit. - Private GitHub repos can be accessed via existing git credentials or optional
GITHUB_TOKEN/GH_TOKENfor download. - Git fallback tries HTTPS first, then SSH.
- The skills at https://github.com/openai/skills/tree/main/skills/.system are preinstalled, so no need to help users install those. If they ask, just explain this. If they insist, you can download and overwrite.
- Installed annotations come from
$CODEX_HOME/skills.
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
pufferlib
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
fluidsim
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
metabolomics-workbench-database
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
astropy
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.
Didn't find tool you were looking for?