Skip to content
cccskills
FreePublished on GitHub

doccano

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

The English summary is being prepared. Showing the original description.

Works withDocker
See how to install

Files included (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 (original)

Review the instructions the agent will receive before you install.

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.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

3ddfa

Free

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

acme

Free

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill338Updated Sep 3, 2026

See all skills by VectorSpaceLab

Report a problem with this skill