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oryxflow is built for AI coding agents — documentation included

Most libraries are documented for humans and then discovered by agents by accident. oryxflow is written for both. Two halves:

  • The library gives an agent the discipline it can't hold in its head — every step cached with an identity derived from its parameters and its code, so it reuses expensive results and can't build on stale ones. That's the story in Claude Code for data science.
  • The documentation gives an agent something it can actually read, in bulk, and trust. That's this page, collected in one place so you don't have to take "AI-friendly" on faith.

Read the entire documentation in one request

The docs publish the llms.txt convention, so an agent doesn't have to crawl page by page and guess what it missed:

URL What it is Use it for
/llms.txt A sectioned index — every page, grouped, with a one-line description of the library Cheap orientation: the agent picks the pages it needs
/llms-full.txt The complete documentation concatenated into a single file One request, whole corpus — no crawling, nothing missed

The fastest way to make any agent competent at oryxflow is to hand it the second URL at the start of a session:

Read https://docs.oryxflow.dev/llms-full.txt, then convert my script in analysis.py
into oryxflow tasks.

Both files are regenerated on every deploy, so they never describe an older version of the library than the one you installed.

The examples an agent reads first are executed by the test suite

An agent that reads a broken example writes broken code with total confidence. So the pages an agent learns the library from aren't just illustrative — the home page, the quickstart and the I/O formats guide are compiled into real pytest files and run top-to-bottom on every build. If one stops working, the build fails and it gets fixed; it doesn't sit there misleading people.

That's the core learning path, so you can tell an agent to copy a pattern from it and expect it to run. To be straight about the boundary: examples on the deeper guide pages and the blog aren't all executed, so treat those as illustrative — and the machine-readable API reference below is generated from the source either way.

The API reference is generated from the docstrings — all of them

API Reference is built from the source, not maintained alongside it, so it can't drift. 100% of the public API carries a docstring, with arguments, return values and a usage example where the call isn't obvious.

This matters for agents specifically: docstrings are available at runtime, so an agent working in a REPL or a notebook can check a signature without leaving the process, and without inventing an argument that doesn't exist.

help(oryxflow.requires)      # what it does, what it takes, and an example
help(oryxflow.runLoad)

Answer engines get a machine-readable description of the project

Every page carries structured data (JSON-LD): the library as a single identified entity cross-linked to its GitHub and PyPI identities, each page as an article, and the pages with a Q&A section as a FAQPage. Every answer in that structured data is generated from the visible prose, so an assistant quoting oryxflow quotes what the page actually says.

Canonical URLs are stamped on every page and the sitemap's freshness dates come from git commit history, so an agent or crawler comparing versions sees one authoritative copy with an honest last-modified date.

The conventions ship as a skill, not just prose

Reading the docs is the general-purpose route. For Claude Code there's a shortcut: the oryxflow plugin installs a skill that auto-activates in a pipeline project, so the agent applies the conventions as it writes instead of needing to be reminded. The slash commands scaffold a project, migrate an existing script, and check an existing project against the standards.

Projects scaffolded by the plugin also get a CLAUDE.md describing the layout and rules — which any agent that reads repo instructions can follow, not only Claude Code.

Frequently asked questions

Does oryxflow have an llms.txt? Yes. /llms.txt is a sectioned index of every page, and /llms-full.txt is the entire documentation concatenated into one file, so an agent can read the whole thing in a single request instead of crawling page by page.

How do I get an AI coding agent to write correct oryxflow code? Point it at https://docs.oryxflow.dev/llms-full.txt once at the start of the session, or install the Claude Code plugin, which loads the conventions automatically. Both work because the examples an agent reads first — the home page, the quickstart and the I/O formats guide — are executed by the test suite on every build, so what it copies is code that actually runs.

Can I trust the code examples in the oryxflow docs? The core learning path is unit-tested: the home page, the quickstart and the I/O formats guide are compiled into pytest files with phmdoctest and run top-to-bottom on every build, so an example that stopped working fails CI instead of quietly misleading you or your agent. Examples on the deeper guide pages and blog posts are not all executed.

Does oryxflow work with agents other than Claude Code? Yes. The machine-readable docs, the tested examples and the docstrings are plain files on a public site, so any agent or assistant can use them. The Claude Code plugin is a convenience that packages the same conventions as a skill; the library itself is an ordinary Python package.

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