---

name: cvat description: "Operate CVAT for computer-vision annotation, dataset workflows, SDK/CLI automation, auto-annotation, and self-hosted deployment." metadata: disco-role: operating disable-model-invocation: true license: MIT

CVAT repo skill

Use this repo skill when a task involves CVAT (Computer Vision Annotation Tool): self-hosted computer-vision annotation, task/project automation, dataset import/export, Python SDK scripts, cvat-cli, auto-annotation functions, serverless model deployment, backups, frames, organizations, or deployment troubleshooting.

Fast routing

User intent	Read
Python automation with cvat_sdk, profiles, Client, task/project objects, uploads, downloads, backups, background operations	sub-skills/sdk-automation/SKILL.md
Terminal automation with cvat-cli, profiles/config, task/project commands, native function commands, JSON shell scripting	sub-skills/cli-automation/SKILL.md
Dataset formats, import/export, images included/excluded, manifests, frame downloads, backups, PyTorch dataset adapter	sub-skills/dataset-ops/SKILL.md
Auto-annotation functions, annotate_task, task auto-annotate, native functions, Nuclio/serverless CPU/GPU models	sub-skills/auto-annotation/SKILL.md
Docker Compose, serverless overlay, admin user creation, CVAT_HOST, CVAT_VERSION, Helm/Kubernetes orientation, service failures	sub-skills/deployment-admin/SKILL.md

Repo-level references

• Read references/cvat-overview.md for the product surface, package map, optional extras, and workflow boundaries.
• Read references/troubleshooting.md for cross-cutting CVAT install/import/auth/version/service issues before drilling into a sub-skill.
• Read references/repo-provenance.md when checking whether this generated skill is stale for a newer CVAT checkout or package version.
• references/repo-routing-metadata.json is structured metadata for managed repo-skill routing.
• Run scripts/cvat_env_check.py for safe local package/CLI import checks; it does not contact a server unless extended by a user.

Installation anchors

For automation against an existing CVAT server:

pip install cvat-sdk cvat-cli

Minimal verification after install:

python scripts/cvat_env_check.py --skip-cli-help

Use optional SDK extras only for selected workflows:

pip install "cvat-sdk[masks]"
pip install "cvat-sdk[pytorch]"

For self-hosted CVAT Community deployment, route to sub-skills/deployment-admin/SKILL.md before suggesting commands that start containers, build images, mutate databases, expose a host, or delete volumes.

Safe defaults

• Prefer Personal Access Tokens and saved profiles over password strings in commands or code.
• Include the server URL scheme (http:// or https://) and explicitly set organization context when operating in a team workspace.
• Treat task/project delete, annotation overwrite, import, backup restore, Docker volume removal, and serverless deployment as side-effecting operations.
• Use a tiny task/archive/image sample before scaling import/export, auto-annotation, or dataset conversion.
• Do not assume GPU/serverless/Docker availability from a Python SDK import check; those are separate backend/service gates.

When not to use this skill

• Pure image modeling/augmentation tasks that do not involve CVAT data, APIs, CLI, annotation, or deployment.
• General Kubernetes/Docker administration with no CVAT-specific files, variables, or service names.
• Text annotation platform workflows unrelated to CVAT's computer-vision data model.