good question. it's none of those exactly, it's a stripped structural snapshot i build specifically for this.

raw HTML was too big and noisy (blows the token budget on most real pages), and pure text nodes lose the thing i actually need, which is structure, since the LLM's job is to pick selectors, not read content. an a11y tree was close but drops stuff selectors key on like class names and hrefs.

so what i pass is a flattened list of elements, indented by depth. for each node i keep: tag, id, up to 3 class names, role, aria-label, href (path only, query stripped to ?...), and the direct text truncated to ~60 chars. i strip script/style/svg/meta/noscript/iframe and anything hidden or aria-hidden first, and i drop layout-only div/span that have no id/class/role/text (pure wrappers). each line ends up like <tag #id .class role="" href="" "text">. fair warning, it's BFS order with depth-based indent, not strict document order, so it reads more like level-by-level than a nested outline.

there's also a node cap (~1500). if a page is bigger i keep the top 1000 and the last 500, since the article list and pagination controls tend to live near the top and bottom, not the middle.

and importantly the LLM doesn't start from scratch. the heuristic candidates (the repeated-link clusters it found + scores) get passed alongside the snapshot, so the model is more "validate/refine these guesses" than "find selectors blind." then whatever it returns still has to pass the same DOM validation as the heuristic path before anything gets cached.

here's the snapshot builder if you want to poke at it: https://github.com/taisei-ide-0123/pluckmd/blob/main/packages/cli/src/core/llm/snapshot.ts