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For LLM assistants

LazyBridge ships a Claude Skill, an llms.txt index, and a llms-full.txt corpus dump so AI coding assistants (Claude, ChatGPT, Cursor, Windsurf, ...) can write LazyBridge code from the authoritative current API rather than from training-data snapshots.

What's available

Surface Where Use it for
Claude Skill Bundled with pip install lazybridge at lazybridge/skill/ Claude Code / Claude API / Claude.ai gets canonical-form guidance whenever it edits LazyBridge code
llms.txt https://core.lazybridge.com/llms.txt Any tool following the llmstxt.org convention — discovers every doc page with one fetch
llms-full.txt https://core.lazybridge.com/llms-full.txt The whole docs corpus concatenated; paste into a long-context model when you don't have a tool that supports llms.txt

Claude Skill

The skill is shipped with the library, not as a separate download — when pip install lazybridge lands in your venv, the skill lands too. See Claude Skill install for the one-line symlink that makes Claude Code pick it up.

The skill teaches:

  • The mental model (Engine + Tools + State).
  • The canonical-first style rule (Agent(engine=LLMEngine("…")), not the string-positional sugar).
  • Every sugar form mapped to its canonical equivalent (the Canonical vs sugar reference, embedded for offline lookup).
  • Anti-patterns: asyncio.run(main()) wrapping, hand-written tool schemas, .text() on structured output, redundant from_* factories (deleted in 0.7.9).

llms.txt and llms-full.txt

Both are auto-generated at build time from the llmstxt.org-compliant plugin mkdocs-llmstxt.

  • llms.txt is the index — a concise list of every doc page, grouped by section (Concepts / Guides / Recipes / Decisions / Reference / Errors). Tools that follow the convention discover the corpus from this single URL.
  • llms-full.txt is the whole corpus concatenated — currently in the 10-15k-line / 500-800 KB range and growing with each release. Paste it into a long-context model when you don't have a tool that resolves llms.txt for you. Treat the size as a lower bound; if your model has a 128k-token window you'll fit comfortably, anything smaller and you should prefer the per-page llms.txt resolution path.

See llms.txt explained for the format spec, who's adopting it, and what to expect when an assistant fetches it.

Why three surfaces?

Different assistant ecosystems have different conventions:

  • Claude ecosystem reads Skills natively. The skill is the highest-fidelity path: it carries enforcement rules ("write canonical form first", "skip asyncio.run") that a documentation page can't enforce.
  • OpenAI / generic LLMs that follow the llmstxt.org convention fetch llms.txt and resolve linked pages.
  • Long-context models (any provider) take llms-full.txt directly into the context window when the user wants the whole corpus available without per-page resolution.

All three are kept in sync: the skill is hand-authored from the same code-vs-docs audit that produced the rest of the site; llms.txt and llms-full.txt are mechanically regenerated on every build.

See also