Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use Argilla 2.x for human-feedback dataset annotation, Python SDK dataset workflows, server deployment/operations, webhooks, and legacy v1/Rubrix migration.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
| User request signal | Read this first | Why |
|---|---|---|
| Create/configure an Argilla dataset, fields/questions/metadata/vectors, log records, map columns, query/filter/export, use users/workspaces, or build a webhook listener | sub-skills/python-sdk/SKILL.md | Current 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 reindexing | sub-skills/server-ops/SKILL.md | Server package, CLI, service stack, config, and operational troubleshooting |
| Migrate old Argilla v1/Rubrix users, workspaces, or task-specific legacy datasets into Argilla 2.x | sub-skills/legacy-migration/SKILL.md | Safe v1 compatibility and dataset schema migration guidance |
| Check whether this skill matches a checkout/version | references/repo-provenance.md | Source commit, package versions, evidence paths, and refresh baseline |
| Need a high-level capability/dependency overview before choosing a route | references/package-overview.md | Monorepo layout, package split, selected/excluded scope, and dependencies |
| Cross-cutting install/import/API-url/service troubleshooting | references/troubleshooting.md | Quick triage and route-to-sub-skill guidance |
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.
rg.Argilla(api_url=..., api_key=...) client before constructing resources unless environment defaults are guaranteed.argilla-v1 extras into a current Argilla 2.x server environment.scripts/check_argilla_env.py checks package importability and versions and optionally performs a live client.me check.sub-skills/python-sdk/scripts/build_dataset_template.py generates a dry-run-safe SDK dataset template.sub-skills/python-sdk/scripts/webhook_listener_template.py provides a webhook listener skeleton that is dry-run by default.sub-skills/server-ops/scripts/check_server_cli.py renders safe argilla_server CLI help without starting services.sub-skills/server-ops/scripts/docker-compose.argilla.local.yaml is an adapted local Compose template; replace sample credentials and validate before running.sub-skills/legacy-migration/scripts/legacy_migration_skeleton.py prints a safe TODO-based migration plan/template.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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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.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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