---

name: wandb description: "Use W&B's Python SDK and CLI for experiment tracking, offline/local workflows, artifacts and registries, Public API exports, automations, sweeps, and Launch jobs." disable-model-invocation: true metadata: disco-role: operating license: MIT

W&B Repo Skill

Use this skill when a task involves the wandb Python package, the wandb/wb CLI, experiment tracking, local/offline run management, artifacts/model registry, Public API queries, automations, hyperparameter sweeps, or W&B Launch jobs.

Install and Verify

For ordinary users, install the public package:

python -m pip install wandb
python - <<'PY'
import wandb
print(wandb.__version__)
print(wandb.init)
PY

Use optional extras only when the workflow needs them:

• Cloud artifact storage: wandb[aws], wandb[gcp], or Azure storage/identity packages.
• Rich media logging: wandb[media] plus framework/media libraries used by the data.
• Sweeps/Launch/cloud queues: wandb[sweeps], wandb[launch], and provider SDKs or container tooling required by the target backend.
• Workspaces or model helpers: wandb[workspaces] or wandb[models] when a task explicitly uses those surfaces.

For a local source checkout, expect compiled helper artifacts such as wandb-core to be built. If source installation fails, read references/troubleshooting.md before broadening dependencies.

Run the bundled environment check when you need a safe local diagnostic:

python scripts/check_wandb_environment.py --check-cli

Route by Workflow

• Experiment tracking in Python: Use sub-skills/experiment-tracking/SKILL.md for wandb.init(), Run.log(), config, summary, tables/media/plots, metric axes, notebooks, offline/disabled modes, and tracking smoke checks.
• CLI and local/offline workflows: Use sub-skills/cli-and-local-workflows/SKILL.md for wandb login, wandb init, wandb status, wandb offline/online/disabled, wandb sync, local run directories, environment variables, cache cleanup, Docker wrapping, and self-hosted host configuration.
• Artifacts and registries: Use sub-skills/artifacts-and-registries/SKILL.md for wandb.Artifact, Run.log_artifact(), Run.use_artifact(), artifact aliases/versions, downloads, wandb artifact ..., storage references, registry linking, and model registry troubleshooting.
• Public API and automations: Use sub-skills/public-api-and-automation/SKILL.md for wandb.Api, querying runs/projects/sweeps/files/reports/artifacts, exporting history, pagination, and automation events/filters/actions/scopes.
• Sweeps and Launch: Use sub-skills/sweeps-and-launch/SKILL.md for sweep YAML/JSON, wandb.sweep(), wandb.agent(), wandb sweep, wandb agent, Launch jobs, queues, launch agents, resource backends, and structural config validation.

Shared References

• references/repo-provenance.md: read before deciding whether this skill is current for a different W&B checkout or before running a refresh.
• references/troubleshooting.md: use for cross-cutting install/import, authentication, base URL, optional dependency, source-build, and local service issues.
• scripts/check_wandb_environment.py: safe diagnostics for installed package version, importability, CLI availability, optional extras, and selected environment variables with secrets redacted.

Safe Defaults

• Prefer with wandb.init(...) as run: so runs finish even when code raises.
• Use mode="offline" for credentials-free testing and local-only capture; sync later with CLI guidance.
• Never print API keys or .netrc contents. Treat WANDB_API_KEY as present/absent only.
• Use full W&B paths such as entity/project/run_id or entity/project/artifact:alias when defaults could resolve to the wrong account.
• For destructive commands such as wandb sync --clean or cache cleanup, explain what will be deleted and ask before running them.
• Do not invent cloud credentials, queues, Kubernetes contexts, container registries, IAM roles, bucket names, or self-hosted server URLs.

Common Cross-Workflow Paths

• If a training script needs both tracking and datasets/models, start with experiment-tracking, then route artifact inputs/outputs to artifacts-and-registries.
• If an offline run must be uploaded later, instrument with experiment-tracking and use cli-and-local-workflows for wandb sync.
• If a report/export task references existing runs, use public-api-and-automation; if it needs to create future runs, route back to experiment-tracking.
• If a sweep trial logs metrics or artifacts, use sweeps-and-launch for orchestration, experiment-tracking for trial logging, and artifacts-and-registries for data/model lineage.

Freshness Check

This skill was generated from repository evidence and live package inspection. Read references/repo-provenance.md if the current checkout, package version, public CLI/API, tests, or docs have changed. If the provenance differs materially, refresh the skill from the repository before relying on exact signatures or command behavior.