---

name: "project-workflows" description: "Operate spaCy project.yml workflows safely with local validation, dry runs, and explicit network boundaries." disable-model-invocation: true metadata: disco-role: operating license: MIT

project-workflows

Use this sub-skill for project-level spaCy orchestration: cloning templates, fetching assets, running commands and workflows, generating docs, generating DVC config, and pushing or pulling outputs from remotes.

Read or run these first

• references/project-workflows.md — read this for the command map, safe vs side-effect boundaries, dependency/output tracking, and workflow order.
• references/project-yml-reference.md — read this for project.yml keys, command and asset field rules, and requirements checks.
• references/troubleshooting.md — read this when clone, assets, run, document, push, pull, or DVC fails or warns.
• scripts/validate_project_yml.py — run this on a local project file before any networked or remote command.

Default operating order

1. Validate the project file locally.
2. Inspect or generate documentation.
3. Use project run --dry before the first real run.
4. Fetch assets only when you know whether --extra is needed.
5. Run the narrowest command that covers the change.
6. Use push, pull, and dvc only after remotes or DVC are confirmed.

Route elsewhere when the work shifts

• If the project command centers on training config, spacy train, spacy convert, spacy evaluate, or spacy package, use training-and-cli.
• If the issue is package import, installation, or model download, use install-and-inspect.
• If a project script defines custom pipeline components or factories, use pipeline-components.

Verification focus

• project-document-dry-run: local temp project, project document, and project run --dry.
• project-assets-remote: help-only and skip-network checks for project assets, push, and pull.

Boundaries

• Do not treat external template cloning as a validation step.
• Do not assume remote storage, credentials, or network access are available.
• Do not move into training internals or model-serving integrations here.