Plugin commands

The plugin adds five slash commands. Most of the time you won't need them explicitly — the oryxflow skill activates on its own inside a project — but they're the fast path for the common setup and maintenance jobs.

• /oryxflow:init-project — set up a ready-to-run project structure in an empty directory, so you start writing tasks straight away instead of building the folders, files, and conventions by hand.
• /oryxflow:migrate — restructure an existing ad-hoc analysis (monolithic notebooks, linear scripts, hardcoded paths) into a cached, parameterized oryxflow pipeline, one task at a time — so you get reproducibility and caching without a risky big-bang rewrite.
• /oryxflow:init-gitlfs — put data/ under Git LFS, so you version and share your data as easily as your code — teammates clone the repo and get the exact datasets each run produced.
• /oryxflow:update-project — bring an older project up to the current project structure, so you pick up the latest conventions and layout without a manual migration.
• /oryxflow:check-standards — check names, style, and docstrings against the house standards, so the codebase stays consistent and easy for teammates (and the AI) to navigate and extend.

The migration path most people want

If you already have a notebook or script that works, /oryxflow:migrate is the on-ramp. It converts the analysis into tasks incrementally — each step becomes a cached, parameterized task with its dependencies wired — so at every point you have a working pipeline, not a half-rewritten one. The end state is reproducible and lineage-tracked, and the expensive steps stop rerunning on every edit.

See the companion guide Turn a messy notebook into a reproducible pipeline for what that transformation looks like step by step.

After scaffolding

Once the project exists, the skill takes over automatically — it keeps the wiring consistent, verifies your edits actually reran the tasks you expected, and answers staleness warnings the right way. That ongoing discipline is the subject of Trustworthy AI data analysis and Why library + plugin is a matched pair.

The init-project scaffold and the graduated path a growing project follows are covered in data-science project structure; the naming, code-organization, and docstring conventions check-standards enforces are in data-science coding standards.