bundled/skills/.system/skill-installer/SKILL.md
Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos).
npx skillsauth add foryourhealth111-pixel/vco-skills-codex skill-installerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Helps install skills. By default these are from https://github.com/openai/skills/tree/main/skills/.curated, but users can also provide other locations. Experimental skills live in https://github.com/openai/skills/tree/main/skills/.experimental and can be installed the same way.
Use the helper scripts based on the task:
.curated, but you can pass --path skills/.experimental when they ask about experimental skills.Install skills with the helper scripts.
When listing skills, output approximately as follows, depending on the context of the user's request. If they ask about experimental skills, list from .experimental instead of .curated and label the source accordingly:
"""
Skills from {repo}:
After installing a skill, tell the user: "Restart Codex to pick up new skills."
All of these scripts use network, so when running in the sandbox, request escalation when running them.
scripts/list-skills.py (prints skills list with installed annotations)scripts/list-skills.py --format jsonscripts/list-skills.py --path skills/.experimentalscripts/install-skill-from-github.py --repo <owner>/<repo> --path <path/to/skill> [<path/to/skill> ...]scripts/install-skill-from-github.py --url https://github.com/<owner>/<repo>/tree/<ref>/<path>scripts/install-skill-from-github.py --repo openai/skills --path skills/.experimental/<skill-name>$CODEX_HOME/skills/<skill-name> (defaults to ~/.codex/skills).--path values install multiple skills in one run, each named from the path basename unless --name is supplied.--ref <ref> (default main), --dest <path>, --method auto|download|git.https://github.com/openai/skills/tree/main/skills/.curated via the GitHub API. If it is unavailable, explain the error and exit.GITHUB_TOKEN/GH_TOKEN for download.$CODEX_HOME/skills.development
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
development
Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.
development
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
development
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.