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Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path.
Passed 9 of 17 test cases with the skill, and 7 of 17 without it.
and 3 more in the full report
6 fixed6 passed either way7 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
The instructions your model reads when the skill loads.
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:
.curated, but you can pass --path skills/.experimental when they ask about experimental skills.Install skills with the helper scripts.
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}:
After installing a skill, tell the user it will be available on their next turn.
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 jsonscripts/list-skills.py --path skills/.experimentalscripts/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>scripts/install-skill-from-github.py --repo openai/skills --path skills/.experimental/<skill-name>test cases passed, vs 7 / 17 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.