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).
- License 许可
- MIT
- 所属仓库
- rexleimo/aios
- 最近更新
- 2026-11-07
- Stars
- ★ 62
- Forks
- 11
01 前言 Front Matter
- name
- skill-installer
- tags
- null
- scopes
- null
- clients
- null
- metadata
- { "short-description": "Install curated skills from openai/skills or other repos" }
- description
- 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).
- repoTargets
- [ "codex" ]
- defaultInstall
- null
- installCatalogName
- null
- targetRelativePathBySurface
- { "codex": ".system/skill-installer" }
02 资源文件 6
- 参考文档agents/openai.yaml221 B
- 资产文件assets/skill-installer-small.svg923 B
- 许可证LICENSE.txt11.1 KB
- 脚本scripts/github_utils.py659 B
- 脚本scripts/install-skill-from-github.py9.9 KB
- 脚本scripts/list-skills.py2.9 KB
03 SKILL.md 正文
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.
04 同仓库更多 Skills
versioning-by-impact
Use when completing a task and deciding whether repository changes require a semantic version bump and changelog entry before commit/push.
verification-loop
Evidence-before-assertions workflow. Use before claiming work is done, before release, and after any behavior change in scripts/skills/MCP.
typesafe-ai
Build AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.
skill-opt-lite
Train and optimize skill documents using the SkillOpt methodology — the agent acts as both target and optimizer, running tasks, reflecting on failures, proposing edits, and validating improvements in a self-contained training loop. TRIGGER: optimize skill, train skill, improve skill, skill training, skill optimization, SkillOpt, skill evolution, skill iteration loop, skill feedback loop, skill tuning.
skill-constraints
Enforce browser automation safety constraints — tool usage order, anti-detection rules, screenshot discipline, and human-in-the-loop checks. Use when setting up or verifying browser/MCP tool operations, NOT for every skill execution.
seo-geo-page-optimization
Use when improving a single page for SEO and AI answer-engine visibility (GEO), especially when you need faster first-screen conversion and clearer traffic funnels.