oryxflow glossary

Short, plain-language definitions of the terms used across these docs and in oryxflow itself — the vocabulary of reproducible data science, from task DAGs and lineage through to Claude Code plugins and skills. Each entry links to where the concept is covered in full.

What is a reproducible pipeline?

A reproducible pipeline is an analysis where every output can be traced to the exact code, parameters, and inputs that produced it — so you can recreate any result on demand. oryxflow makes your data-science work reproducible by default: each step is a task whose identity comes from its code and its inputs, so a result is never attributed to code that didn't produce it. Its output is saved under that identity, which is also what makes regenerating it cheap. See Why oryxflow.

What is data lineage (provenance)?

Data lineage — also called provenance — is the record of what ran, when, with which parameters and code, and why it recomputed. It turns "is this result stale?" and "was it built with the current code?" into queries instead of guesses. oryxflow writes this record automatically as you run. See Why oryxflow.

What is code-change invalidation?

Code-change invalidation means a pipeline reruns a step when its code changes — not only when its parameters or data change. oryxflow compares what your code does, not how it's written, so edits to comments or formatting are ignored, while a real logic change reruns that step and everything downstream. See Managing workflows.

What is a task DAG?

A task DAG (directed acyclic graph) is your analysis expressed as steps — tasks — connected by their dependencies, with no cycles. You declare each task; the engine works out the order, so a step can never run before the step it depends on. A change reruns exactly what it affects, and everything it doesn't affect is served from its saved output. See Writing tasks.

What is the difference between a Claude Code plugin, a skill, and a slash command?

They're nested, not competing. A plugin is the installable package you add to Claude Code. A skill is one thing a plugin can contain — a bundle of instructions and conventions the agent loads on its own when the context matches, without you invoking anything. A slash command is an action you invoke explicitly, like /oryxflow:init-project. A plugin can also ship hooks and other pieces.

oryxflow ships a plugin containing the oryxflow skill (the data-science conventions and cache disciplines, which auto-activate when you edit pipeline files) plus a handful of slash commands you call deliberately — /oryxflow:init-project to scaffold, /oryxflow:migrate to convert an existing script or notebook, and a few more. It is not an MCP server. In practice you install the plugin once and the skill just works in the background while you describe the analysis you want. See Build with Claude Code and the command reference.

What is a cached intermediate?

A cached intermediate is the saved output of a pipeline step, reused instead of recomputed on the next run. oryxflow saves every task's output under the identity of the code and parameters that made it, so an intermediate can't be handed to you as current once either has changed — the thing that goes wrong with hand-rolled pickle files. Reuse is the payoff: re-running a pipeline only pays for what actually changed. See Task I/O formats.

What is a parameter sweep?

A parameter sweep runs the same analysis across many configurations — model × features × window — to compare results. In oryxflow each configuration's output is kept separately under the parameters that produced it, so the comparison is between results you can each trace back, not files that overwrote one another. Only what a configuration actually changes is recomputed — the shared upstream steps run once — so a sweep costs far less than re-running everything per combination. See Parameter sweeps without rerunning.

What is a task id and task family?

Every oryxflow task has a task family (its name) and a task id that also encodes its parameters, so two runs with different parameters are distinct cached outputs you can tell apart. This is how oryxflow keeps results from different configurations from overwriting each other. See Parameters.