본문으로 건너뛰기
cccskills
무료GitHub에 공개

argilla

Use Argilla 2.x for human-feedback dataset annotation, Python SDK dataset workflows, server deployment/operations, webhooks, and legacy v1/Rubrix migration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(24)

  • SKILL.md5.0 KB
  • references/package-overview.md3.7 KB
  • references/repo-provenance.md2.6 KB
  • references/repo-routing-metadata.json306 B
  • references/troubleshooting.md3.9 KB
  • scripts/check_argilla_env.py3.6 KB
  • sub-skills/legacy-migration/references/legacy-api-reference.md6.3 KB
  • sub-skills/legacy-migration/references/migration-workflow.md8.7 KB
  • sub-skills/legacy-migration/references/troubleshooting.md6.9 KB
  • sub-skills/legacy-migration/scripts/legacy_migration_skeleton.py12.8 KB
  • sub-skills/legacy-migration/SKILL.md3.1 KB
  • sub-skills/python-sdk/references/api-reference.md12.8 KB
  • sub-skills/python-sdk/references/data-formats.md7.8 KB
  • sub-skills/python-sdk/references/troubleshooting.md8.6 KB
  • sub-skills/python-sdk/references/workflows.md11.5 KB
  • sub-skills/python-sdk/scripts/build_dataset_template.py8.6 KB
  • sub-skills/python-sdk/scripts/webhook_listener_template.py7.5 KB
  • sub-skills/python-sdk/SKILL.md3.2 KB
  • sub-skills/server-ops/references/deployment.md9.5 KB
  • sub-skills/server-ops/references/server-cli-and-config.md11.5 KB
  • sub-skills/server-ops/references/troubleshooting.md8.0 KB
  • sub-skills/server-ops/scripts/check_server_cli.py3.6 KB
  • sub-skills/server-ops/scripts/docker-compose.argilla.local.yaml3.6 KB
  • sub-skills/server-ops/SKILL.md3.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Argilla repo skill

Argilla is a self-hosted/open-source collaboration platform for building high-quality datasets for AI projects. Use this skill when a task involves Argilla 2.x SDK code, annotation dataset schemas, record logging, search/filter/export, Argilla server deployment, webhooks, or migration from Argilla v1/Rubrix.

The source project is mature/stable; prefer current documented Argilla 2.x workflows for new work and route legacy APIs only through the migration sub-skill.

Fast route map

User request signalRead this firstWhy
Create/configure an Argilla dataset, fields/questions/metadata/vectors, log records, map columns, query/filter/export, use users/workspaces, or build a webhook listenersub-skills/python-sdk/SKILL.mdCurrent Argilla 2.x SDK API and dataset-feedback workflows
Deploy or operate the server with Hugging Face Spaces, Docker Compose, Kubernetes/Helm, python -m argilla_server, database/search/Redis, OAuth/SSO, telemetry, proxy/base URL, or reindexingsub-skills/server-ops/SKILL.mdServer package, CLI, service stack, config, and operational troubleshooting
Migrate old Argilla v1/Rubrix users, workspaces, or task-specific legacy datasets into Argilla 2.xsub-skills/legacy-migration/SKILL.mdSafe v1 compatibility and dataset schema migration guidance
Check whether this skill matches a checkout/versionreferences/repo-provenance.mdSource commit, package versions, evidence paths, and refresh baseline
Need a high-level capability/dependency overview before choosing a routereferences/package-overview.mdMonorepo layout, package split, selected/excluded scope, and dependencies
Cross-cutting install/import/API-url/service troubleshootingreferences/troubleshooting.mdQuick triage and route-to-sub-skill guidance

Minimal install and import checks

For current SDK-only workflows:

python -m pip install argilla
python - <<'PY'
import argilla as rg
print(rg.__version__)
print(rg.Argilla)
PY

For server operations, the server package is separate in source and deployments often use Docker/Spaces. If installed as a Python package, check it without starting services:

python - <<'PY'
import argilla_server
print(hasattr(argilla_server, "app"))
PY
python -m argilla_server --help

Run scripts/check_argilla_env.py for a safe local import/version check. Add --check-server only when the user intentionally wants a live API check and provides a reachable API URL/key.

Operating rules

  1. Treat Argilla as a service-backed package. Dataset creation, record logging, user/workspace changes, Hub import/export, webhook registration, server starts, migrations, reindexing, and deployment commands can mutate data or contact external services.
  2. For SDK work, create an explicit rg.Argilla(api_url=..., api_key=...) client before constructing resources unless environment defaults are guaranteed.
  3. For private Hugging Face Spaces, keep the Argilla API key separate from the Hugging Face bearer token header.
  4. For deployment work, validate configs and render CLI help before starting Docker/Kubernetes/server processes or running migrations.
  5. For legacy migration, export/back up first; do not install broad old argilla-v1 extras into a current Argilla 2.x server environment.
  6. Do not route frontend implementation changes, repository CI, docs-generation, or heavy old notebooks through this runtime skill.

Bundled safe helpers

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

3ddfa

무료

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

3ddfa-v2

무료

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

ab3dmot

무료

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

acme

무료

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3382026년 9월 3일 업데이트

VectorSpaceLab의 스킬 모두 보기

이 스킬의 문제 신고