Which layer answers which question

Every llms family has a ladder of layers — index, split root, small, full, facts, topical, vocabulary — and beside them two retrieval modes over the facts, keyword and vector. The cheapest layer that answers your question is the right one. This table matches the shape of a question to that layer and to the recipe that shows it. Recipes are copy-only in this step: the code illustrates, the cost line states what was measured or estimated, and each recipe closes with the note that it becomes runnable in the playground step.

The table

question shape	layer	cost class	recipe
Orientation before any retrieval: what does this site cover, where do I start	`llms.txt` (≤ 10 KB) then ≤ 2 hops to a `.md` twin	~3k tokens, 3 requests, 0 embeddings	recipe-01
Orientation on a site whose index split into sections (`## Sections` present)	split root: root index → `<slug>/llms.txt` → page	~3–5k tokens, 3–4 requests	recipe-02
An exact token: an env var, a flag, a header name, an error string	keyword layer (`mode="keyword"`, FTS5/BM25) over `llms-facts.txt`	microseconds, 0 model tokens, 0 embeddings	recipe-03
A paraphrased question, or mixed / unsure whether the words match the source	hybrid (`mode="hybrid"`, RRF over keyword + vector), or vector alone (`layer="facts"`)	1 embedding, 0 generation tokens	recipe-04
An agent that must find the right page from an MCP client without a search index	index-first via `hub_docset_index` → `sections` → section index → page	~2k tokens read per hop, 0 embeddings	recipe-05
A scripted check or query from a shell or a CI step	the `llmsx` CLI (today: the hub scripts it wraps)	seconds; 0 model tokens for lint / keyword	recipe-06
Citation-grade answers inside your own RAG store	`llms-facts.txt` units, one document each, `url#anchor` as metadata	1 embedding per unit at ingest; ~845k tokens for a 191-page site	recipe-07
Keeping a published file honest on every push	the lint as a GitHub Action gate (exit 1 on High)	~10 s per file; network only with `--check-links`	recipe-08
Serving the files so agents and the lint can find them	headers: `text/markdown`, `X-Markdown-Tokens`, `Link: rel="describedby"`, `rel="alternate"` on HTML	one config block; verify with `curl -I`	recipe-09
Whole-corpus reasoning, offline and private, within a token budget	a local hub: Ollama + indexer + keyword layer + `llms_serve.py`; `llms-small.txt` for budgeted reads	one machine; ~50k tokens per small read, 0 API spend	recipe-10
One concept across many sources, disagreements visible	a topical file (`/t/<slug>/`) built from a fact pool	minutes to build; `--no-embed` for 0 embeddings	recipe-11
Disambiguation: which sense of a word this family means, and its aliases	`llms-vocabulary.txt` senses and `aka:` expansion before FTS5	free: string match, 0 model tokens	recipe-12

How to read it

Start at the top. The first three rows are almost always enough. An index read plus two hops answers “what is here”; a keyword lookup answers “what is the exact flag”. Only when the words in your question may not be the words in the source do you pay for an embedding.
Cost class is honest, not precise. Token counts are chars/4, the same estimator the hub writes into manifest.json. Where a recipe has a measured figure it says measured; where it does not, it says estimated. The CI in a later step replaces every estimate with a run.
Two shapes have no recipe of their own. Whole-corpus reasoning against a hosted family is hub_docset_index(docset, file="llms-small.txt") (recipe-05 shows the call); a full-file read is hub_llms_full_read(key, page=…) for one page at a time (recipe-03 shows it). Both are one call, not a recipe.
Every layer’s numbers, for one real family. From outputs/exports/code.claude.com.llms/manifest.json (191 pages, acquired from the publisher’s llms-full.txt): root index ~280 tokens; the three section indexes ~167, ~1,615 and ~1,388 tokens; llms-small.txt ~49,785 tokens; llms-full.txt ~2,097,403 tokens; llms-facts.txt 14,031 units, ~844,553 tokens. The gap between the first line and the last is the whole argument for reading the index first.

When the table is the wrong tool

If the question is “is this file any good”, none of these rows apply — that is the lint, not a retrieval. If the question is “what do many sites say about X”, the source-axis rows do not apply either; recipe-11 and the CLLMS essay cover the concept axis. And if the corpus is not published as an llms family at all, the first step is to make one (recipe-10 in miniature, docset_refine export at scale), after which every row above starts to work.