Examples

The decision table and the copy-only recipes: which file to reach for, and the exact commands that produce it.

• Which layer answers which question — The decision table for the cookbook: match the shape of your question to the cheapest llms layer that answers it, then open the recipe.
• Recipe 01 — Two hops with requests — Read a site's llms.txt, pick a page by its description, fetch the .md twin, answer. The baseline every other recipe is measured against.
• Recipe 02 — Split root: follow a section index — When the root llms.txt has a ## Sections block, let the counts on each section line decide which section index to fetch before touching a page.
• Recipe 03 — Keyword layer from Claude Code — Find an exact token — an env var, a flag, an error string — with hub_query_docset(mode="keyword"), then open the page it came from. Zero model tokens.
• Recipe 04 — Hybrid: keyword and vector fused — For a paraphrased or uncertain question, mode="hybrid" runs the keyword and vector legs and fuses them with reciprocal-rank fusion; legs == 2 tells you both agreed on a hit.
• Recipe 05 — Index-first agent over MCP — hub_docset_index → read sections → the section's llms.txt → the page. The pattern a concept-tree node page uses to find a source without any search index.
• Recipe 06 — The llmsx CLI — Lint, query, export and inspect the tree from a shell: the llmsx commands and the hub scripts each one wraps today.
• Recipe 07 — Facts into a RAG store — Parse llms-facts.txt with UNIT_RE, one document per unit with its url#anchor as metadata, embed with mxbai-embed-large — and never mix it with a 768-dimension model.
• Recipe 08 — GitHub Action lint gate — Fail a pull request on any High finding in your llms files, and annotate the offending lines from the lint's JSON.
• Recipe 09 — Serving with the right headers — nginx and Cloudflare _headers blocks that serve .md twins as text/markdown with X-Markdown-Tokens and the two Link relations, verified with curl -I.
• Recipe 10 — A local hub in miniature — Ollama, the docset indexer, a keyword layer and llms_serve.py on one machine: the whole retrieval stack for one family, offline and private.
• Recipe 11 — Building a topical file — docset_refine topical turns a fact pool into a concept-axis llms.txt + llms-facts.txt with the subject's child concepts as sections; then /ldo --agent-test checks an agent can actually use it.
• Recipe 12 — Reading a vocabulary — Expand a query through a family's aka: list before the FTS5 lookup, and pin the sense the family means. Free: string matching, no model.