---

name: metaflow description: "Guides Metaflow workflow authoring, local and remote execution, client/data access, cards, dependency environments, deployment orchestration, and repository maintenance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Metaflow Repo Skill

Use this skill when a task involves the metaflow Python package, Metaflow flow scripts, Metaflow client objects, runtime decorators, cards, deployment backends, or maintaining the Metaflow repository. Metaflow is a workflow framework for building, running, observing, scaling, and deploying data science and ML systems from Python FlowSpec classes.

Start Here

1. Install or verify Metaflow in the target environment:

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

2. If automation cannot infer a username, set one before running flow CLIs:

   export USERNAME=${USERNAME:-disco}
   

3. For a local flow script, prefer:

   python flow.py --no-pylint check
   python flow.py run --max-workers 1
   python flow.py version
   

   The top-level metaflow command has no --version option; flow scripts expose version as a subcommand.
4. For a quick public-package diagnostic, run scripts/check_metaflow_environment.py.

Route By Task

User intent	Read
Write, check, run, resume, or debug a local FlowSpec; use Parameter, Config, IncludeFile, foreach, or local decorators	sub-skills/flow-authoring/SKILL.md
Run flows from Python code, notebooks, or subprocess wrappers with Runner, NBRunner, Deployer, or returned ExecutingRun objects	sub-skills/runner-and-programmatic/SKILL.md
Query runs/artifacts/logs/tags through Flow, Run, Task, metadata, namespaces, datastores, or S3 datatools	sub-skills/client-and-data/SKILL.md
Add or inspect Metaflow Cards, current.card, card CLI output, task logs, sidecars, or runtime observability	sub-skills/cards-and-observability/SKILL.md
Use AWS Batch, Kubernetes, Argo Workflows, Step Functions, Airflow, projects, schedules, events, secrets, or remote compute resources	sub-skills/deployment-orchestration/SKILL.md
Configure @pypi, @conda, `--environment=conda	pypi
Modify this repository, choose tests, follow contributor policy, or work with devstack/stubs/R/package maintenance	sub-skills/repo-maintenance/SKILL.md

Shared References

• Read references/api-and-cli-overview.md for verified public imports, high-level CLI groups, and common object names.
• Read references/troubleshooting.md for cross-cutting install/import, username, optional dependency, and source-dependency mistakes.
• Read references/repo-provenance.md before deciding whether this skill is current for a Metaflow checkout or should be refreshed.
• references/repo-routing-metadata.json provides structured routing metadata for managed repo-skill import.

Boundaries

• Local authoring and package inspection are CPU-capable. Cloud, Kubernetes, Argo, Airflow, Azure, GCP, S3 service, devstack, and GPU/PyTorch parallel paths require the relevant credentials, services, or hardware and are not proven by a CPU import.
• This skill is for using or maintaining Metaflow. For unrelated MLOps packages, choose the package-specific skill instead.
• Runtime guidance is self-contained. Do not require future agents to open the original repository checkout; use the bundled references and scripts here.