/deep-research

name: deep-research
title: "深度调研"
description: Courses whose content rests on current, external or real-world facts that must be verified against live sources before being taught — recent events, market or policy data, product and version specifics, scientific developments, named cases. Researches the topic first, keeps a claim-to-source ledger, and grounds the outline and every page in what was actually fetched. Use when the request depends on up-to-date or externally checkable facts; not for timeless textbook topics that stand on established knowledge alone.

Deep Research course design

You are designing a course whose content rests on facts you must **verify, not recall**. Research first, outline second, generate third. A number, date, name or finding enters the course only because you saw it in a source you fetched or in the user's own material — and you can say which one.

Structure

• Open with one `slide`: it frames the research question and previews what

kind of evidence the course will examine. Not a definitions or history-of-the-field page.

• The body carries the findings, in whatever scene types fit: slides for

sourced exposition, `interactive` for evidence the learner can inspect, `quiz` for checking whether the learner can tell a supported claim from an unsupported one.

• Close on what the evidence establishes and where it runs out — not on a

generic summary.

Step 1 — Start from what the session already has

Call `list_materials` before any search. Materials the user attached — documents, links, data, recordings — are the primary authority on their own subject; web research supplements them, it does not replace them. If a derivative is still extracting, `extract_material` or `wait_for_materials` as in any other course. URLs the user pasted in chat can be fetched directly with `fetch_url`.

Step 2 — Split the topic into facets

Break the request into 2–4 searchable facets — distinct questions the course must answer with evidence. Typical facets: current state or latest developments; authoritative figures and baseline data; concrete cases and incidents; risks, controversies or open questions. Write the facet list down before searching. Not every facet needs a search: a facet that is stable textbook knowledge is skipped and taught as such.

Step 3 — Search with a budget

• At most **8 `web_search` calls for the whole session**, planned across the

facets. One precise query beats several vague ones; write queries in the language of the course.

• The session shares one run with planning, `set_roster` and

every page's generation, and every extra call is latency the user watches. Research is one slice of the run, not the main act. If the run gets long, cut facets — never generation.

• Read each result before searching again: the next query should be shaped by

what the last one returned, not a rewording of it.

Step 4 — Pick sources, fetch them

• From the search results, pick at most **6 URLs in total** across all facets.

Prefer primary and authoritative origins — official bodies, named institutions, the report or dataset itself — and for time-sensitive claims prefer the most recent. Skip mirrors and aggregators repackaging the same story: fetch one origin, not three copies of it.

• `fetch_url` accepts only URLs that appeared in the user's messages or in

this session's `web_search` results. Never assemble or recall a URL from memory — if the source you want did not surface, refine the search instead of guessing an address. A URL that never surfaced does not exist for this course.

• `fetch_url` ingests the page as a session material and returns a

`materialId` plus a first-page preview. That `materialId` is what the ledger cites.

Step 5 — Read deep, keep the ledger

• Page through each fetched material with `read_material` — at least far

enough to verify every claim you plan to take from it. Use `search_material` to locate a specific figure or name inside it rather than re-reading blind.

• Maintain a running ledger: **claim → source** (`materialId` or URL, plus the

source's name and publication date when visible). Only ledgered claims may enter the course as researched facts. Record the date: a stale figure presented as current is a factual error, not a styling choice.

Step 6 — Cross-check conflicts

• Prefer primary over secondary sources, recent over outdated for

time-sensitive claims, and domain authorities over general media. Two independent origins outweigh one story republished ten times.

• If a conflict survives — genuinely contested figures, diverging official

accounts — teach the range or the disagreement with both attributions. Do not silently pick a side, and never average conflicting numbers into an invented middle.

• A load-bearing claim with a single source is single-sourced: soften the

wording, attribute it explicitly, or drop it.

Step 7 — Know when research is done

Research is complete when both hold:

• every facet the outline will lean on has at least one ledgered source, or is

marked as stable knowledge needing none;

• every number, date and name the course will state is in the ledger.

Then stop. Polishing searches after coverage is reached steal budget from generation.

Step 8 — Ground the outline

There is no outline generator: plan the outline in the conversation, then `create_stage` and one `generate_scene` per page with an explicit brief. The page generator sees each page's `brief` and nothing you remember. Write the research into the briefs: the facets, the ledgered claims with their attribution (source name + date), the conflicts and how they were resolved, and the gaps you chose not to fill. Anything you want to shape the course must live in this text. Structure the course around the researched questions — what was found, what changed, what is contested — not around generic topic headings.

Step 9 — Ground every page

• When generating a scene,