Search already-cut debate cards. Plain English works ("cards saying data centers raise power bills"), and so does debate shorthand ("econ decline war", "heg", "prolif").

mode: "hybrid" (default: meaning + keywords) or "keyword" (exact words only).
scope: "library" = OpenCaselist corpus (PF, LD, Policy, camp OpenEv files; 2014-2022);
       "mine" = cards the user cut or imported (local installs).
sort: "relevance" or "popular" (most-read by teams first; a strong quality signal for impact cards).
side: "A"/"N" (aff/neg). event: pf | ld | cx | openev. Try a couple of phrasings for important searches.

Card by id (lib:N or cN). view="read" (default, compact): tag, cite, and only the underlined/highlighted text (==highlighted== is read aloud, underlined is context, ... marks skipped text). view="full": the whole body, needed to judge context, find indicts in unhighlighted text, or recut.

Fetch an article/PDF (or re-open a fetched source by its id) as clean numbered paragraphs + citation metadata. Long sources are paged: call again with start_paragraph to continue. Copy quotes for cut_card exactly from this text.

Suggest a card for claim from a fetched source (sN / s_… from fetch_source): the best-matching passage and highlight phrases, all exact source text. Review and adjust, then pass the returned start_quote, end_quote, paragraph, highlight and underline to cut_card with a tag and cite.

Find evidence for a claim in one step: the best already-cut library cards PLUS new cards auto-cut from fresh papers/news (fetched, passage picked, highlighted, verified verbatim). Tags default to the claim: rewrite them to what each highlight proves, and check quals before using a card.

Find NEW evidence to cut: recent scholarly papers (with author affiliations for quals and free PDF links) and current news articles. kinds: ["papers", "news"] (default both). Free and keyless; if a service is busy it is listed under "skipped". Then fetch_source a result's url and cut it.

Cut an evidence card. The body is the exact source text from start_quote through end_quote (a few sentences to a few paragraphs; enough context that the author's meaning is clear). highlight = phrases read aloud; underline = wider phrases that give context (defaults to highlight). Every quote must be copied verbatim from the source (only whitespace, quote marks and dashes are normalized); anything else is rejected with a hint showing where the text stopped matching. source_id: sN from fetch_source, or a card id (cN, lib:N) to re-cut an existing card. paragraph: the [N] number from fetch_source where the card starts; required when start_quote appears more than once in the source.

Write a speech doc / case / block file. items in order, each one of: {"pocket": "..."} {"hat": "..."} {"block": "..."} (Verbatim headings: Pocket > Hat > Block), {"tag": "..."} (analytic tag, no card), {"text": "..."} (speech prose), {"card": "c12" | "lib:345"} (optionally {"card": id, "tag": "new tag"} to retag a card in the export). format "docx": Verbatim-compatible Word file. format "gdocs": an .html file for Google Docs users (open it, select all, copy, paste into a Google Doc; or upload it to Google Drive and open with Google Docs). version "full" (default): complete cards, the doc you send in the email chain / keep as the file. version "read": read-ready copy to speak from (Rhetorify): each card cut to tag, short cite and only its highlighted words, each hat/block heading marked with its time at wpm, total time on top. Returns the file path.

Time a speech exactly and return its read-ready script (what is actually said aloud). Use it to check a case fits before exporting, and to see what to trim. Counting is exact, never estimate word counts yourself. items: the same list as export_doc (headings, {"tag"}, {"text"}, {"card": id}). A card counts its tag, short cite and highlighted words only. For a case pasted as plain text, pass the words the debater actually reads as {"text": ...} items (highlighting in pasted text can't be seen). speech: constructive (4:00), rebuttal (4:00), summary (3:00), final focus (2:00). wpm: the debater's pace. Lay about 160, fast about 200, circuit about 230+. Ask their pace if it matters.

Search OpenCaselist (disclosed cases) for a team, debater last name, or argument text. Defaults to the current HS PF caselist; pass a slug like 'hspf25' for older ones. Limit: 4/min.

A team's caselist page: rounds (tournament, side, opponent, round report, open-source file path) and cites (their disclosed cards). Use for scouting and building blocks against their case.

Scout a whole tournament field: match every entry to its caselist page in one call. entries: the CSV Tabroom exports from a tournament's Entries/Field page (Institution,Location,Entry,Code,...), or one line per team like "Plano West, Park & Jiang". Returns per team: disclosed or not, round count, recent open-source doc paths (for caselist_download) and cite titles. Then follow pf-scout "Prep out a tournament".

Download an open-source .docx from OpenCaselist (the 'opensource' path from caselist_team or a search 'download_path') and import its cards as cN ids you can read, analyze, re-cut or export.

Whether the local card library is built, how many cards per event, years covered, and build progress.

Install the card library in the background (one time; resumable; progress via library_status). mode "download" (default): the prebuilt library (~170k cards incl. the most-read PF, LD, Policy and camp cards; a few minutes). "quick" / "full": build from the raw dataset instead (PF only in minutes, or everything in a few hours).

PF debate knowledge. Read "pf-debate" first. Task guides: pf-case, pf-cut-card, pf-analyze, pf-blocks, pf-blockfile (team block files), pf-scout, pf-practice. References: glossary, format, tactics, impacts, evidence-ethics.