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doccano

Operate doccano for text annotation, dataset import/export, auto-labeling, deployment, and repo maintenance.

中文简介正在准备中。当前显示原文说明。

集成工具Docker
查看安装方法

包含的文件(25)

  • SKILL.md2.8 KB
  • references/cli-reference.md2.9 KB
  • references/overview.md2.4 KB
  • references/repo-provenance.md1.5 KB
  • references/repo-routing-metadata.json303 B
  • references/task-types.md2.3 KB
  • references/troubleshooting.md4.3 KB
  • scripts/cli-smoke.sh710 B
  • sub-skills/auto-labeling/references/troubleshooting.md1.5 KB
  • sub-skills/auto-labeling/references/workflows.md2.1 KB
  • sub-skills/auto-labeling/scripts/list-templates.py777 B
  • sub-skills/auto-labeling/SKILL.md1.8 KB
  • sub-skills/data-transfer/references/formats.md2.8 KB
  • sub-skills/data-transfer/references/troubleshooting.md2.3 KB
  • sub-skills/data-transfer/scripts/list-formats.py1.3 KB
  • sub-skills/data-transfer/SKILL.md1.8 KB
  • sub-skills/project-annotation/references/troubleshooting.md1.9 KB
  • sub-skills/project-annotation/references/workflows.md3.0 KB
  • sub-skills/project-annotation/SKILL.md1.7 KB
  • sub-skills/setup-and-deploy/references/build-and-test.md1.0 KB
  • sub-skills/setup-and-deploy/references/deployment.md1.0 KB
  • sub-skills/setup-and-deploy/references/install-and-runtime.md1.7 KB
  • sub-skills/setup-and-deploy/references/troubleshooting.md1.6 KB
  • sub-skills/setup-and-deploy/scripts/build-package.sh1.1 KB
  • sub-skills/setup-and-deploy/SKILL.md1.9 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

doccano

doccano is a text annotation platform with a Django backend, a Nuxt frontend, and CLI/deployment entry points for local, Docker, and cloud use.

Use this skill when a task mentions doccano itself, project/label/member workflows, dataset import or export, auto-labeling, deployment, or repository maintenance for this checkout.

How to route

RouteRead when
sub-skills/setup-and-deploy/You need install, initialize, run, package, Docker, or cloud deployment guidance.
sub-skills/project-annotation/You need project creation, label setup, members, comments, annotation, cloning, or metrics.
sub-skills/data-transfer/You need dataset import/export formats, validation, encodings, or format-specific troubleshooting.
sub-skills/auto-labeling/You need template selection, request testing, response mapping, label mapping, or auto-labeling activation.

Start here

  • New users usually start with sub-skills/setup-and-deploy/SKILL.md.
  • If the request is about the running app, route to the workflow sub-skill that matches the task family first, then read the shared references listed below.
  • If the request is about editing the source checkout, package build, or tests, keep python-repository-maintenance in mind and start with sub-skills/setup-and-deploy/SKILL.md for the build/runtime entry points.

Safe minimal check

Use the installed package and CLI before deeper work:

pip install doccano
python -I -c "import backend; import backend.cli; print(backend.__file__)"
doccano --help

If the package was already installed, python -m pip check is a useful follow-up health check.

Shared references

  • Read references/overview.md for the repo map and major surfaces.
  • Read references/task-types.md when you need the supported project types or their annotation shapes.
  • Read references/cli-reference.md for the command list and environment variables.
  • Read references/troubleshooting.md for cross-cutting install, runtime, and validation failures.
  • Read references/repo-provenance.md before deciding whether the skill is stale.
  • Read references/repo-routing-metadata.json when you need the routing contract consumed by repo-skills-router.
  • Use scripts/cli-smoke.sh for a fast install-and-CLI sanity check.

Working rules

  • Keep runtime guidance self-contained inside this skill tree.
  • Do not point future agents back to the original checkout for commands that can be bundled here.
  • Prefer the nearest sub-skill for workflow details and keep this router short.
  • Use the repo-maintenance scenario only for source edits, tests, packaging, or contributor workflows.

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