Person research

Three lenses on one person id. momentum gives attention and the shows that mention them most. mentions with mode: "appearances" lists the episodes they were on. search with speakerIds returns their own words; about returns what others said about them.

Run each program with arcmira_execute_read; arcmira_describe has the method reference.

When to use

• prep for an interview, a podcast booking or a meeting with someone
• what has a person said recently, and where
• who talks about a person, and is attention rising

Pick the entity the user meant

Users give names; filters take ids only (ent_..., UC..., 11-character video ids), and a name where an id belongs throws id_required.

• Resolve the exact name the user said, with their own words about it as context (a bare "Theo" has none).
• best and suggested give the id. A suggested pick is an assumption: the answer names it, with suggested.evidence as the reason.
• ask: several entities fit, and ask.options goes back to the user. No answer at all: the name is not in the index.

• Resolve with { type: "person" }. A first name alone ("Sam") matches many people: pass what the user said about them (runs OpenAI, hosts a show) as context, and when resolve still answers ask, show its options.

Worked program

Pass each block to arcmira_execute_read as one program (a block marked arcmira_execute_write goes to that tool), with the name swapped for the user's. It opens with the pick: set CONTEXT to the user's own words about the name. When several entities fit it returns ask and runs nothing else. Show those options to the user, then run it again with ID set to the pick. Build date windows from arcmira.daysAgo(n) and arcmira.today().

Prep on one person

const NAME = "Jensen Huang", CONTEXT = undefined, ID = null;   // CONTEXT: the user's own words about the name, never a guess. After an ask, set ID to the picked option's id and run again
const r = ID ? null : await arcmira.resolve(NAME, { type: "person", context: CONTEXT });
const e = r && (r.best ?? r.suggested);
if (r && !e) return { ask: r.ask };
const id = ID ?? e.id;
const assumed = Boolean(r?.suggested), why = r?.suggested?.evidence ?? null;
const [m, rows, own] = await Promise.all([
  arcmira.momentum(id),
  arcmira.mentions({ entityId: id, mode: "appearances", after: arcmira.daysAgo(90), limit: 40 }),
  arcmira.search({ query: "AI", speakerIds: [id], after: arcmira.daysAgo(180), limit: 5 }),
]);
const about = await arcmira.search({ query: m.entity.name, about: [id], after: arcmira.daysAgo(90), limit: 3 });
const appeared = new Map();
for (const x of rows.mentions) appeared.set(x.media.video_id, { show: x.media.source_channel?.name ?? null, episode: x.media.title, date: x.media.published_at });
let fromAppearance = null;   // no speaker-tagged chunks: read their newest appearance instead
const ep = own.chunks.length === 0 ? rows.mentions[0] : undefined;
if (ep) {
  try {
    const t = await arcmira.transcript(ep.media.video_id, { start: Math.max(0, ep.start_seconds - 5), end: ep.start_seconds + 90 });
    const s = Math.floor(t.lines[0]?.start ?? ep.start_seconds);
    fromAppearance = { episode: t.video.title, show: t.video.channel_name, date: t.video.published_at, url: `${t.video.watch_url}${t.video.watch_url.includes("?") ? "&" : "?"}t=${s}`, lines: t.lines.map(l => `[${Math.round(l.start)}s] ${l.text}`), speaker_labelled: t.lines.some(l => l.speaker) };
  } catch (err) {
    fromAppearance = { episode: ep.media.title, error: err.code };
  }
}
return {
  person: { id, name: m.entity.name, page: m.entity.page, assumed, why },
  attention: { verdict: m.verdict, last_30d: m.volume.mentions_30d, prior_30d: m.volume.mentions_prior_30d, as_of: m.as_of, top_shows: m.top_shows.map(s => [s.channel_name, s.mentions]) },
  appeared_on: [...appeared.values()].slice(0, 8),
  appeared_on_partial: rows.has_more || appeared.size > 8,
  appeared_on_cursor: rows.has_more ? rows.next_cursor : null,
  in_their_words: own.chunks.map(c => ({ said: c.text.slice(0, 300), episode: c.video_title, show: c.channel_name, date: c.published_at, url: c.watch_url })),
  from_their_appearance: fromAppearance,
  said_about_them: about.chunks.map(c => ({ said: c.text.slice(0, 200), show: c.channel_name, date: c.published_at, url: c.watch_url })),
};

A good answer

• Names the person it researched (name and id), and any other person with the same name it set aside.
• Separates what the person said (speaker-filtered) from what others said about them, each with show, date and link.
• Gives the attention verdict, the 30-day count against the prior 30 days, and the top shows, with as_of.
• Lists recent episodes they appeared on, and ends with a few questions or themes that follow from the quotes when the user is prepping.

Traps

• A search with about returns other people talking; only speakerIds returns the person's own words. Speaker tags cover part of the index, so when the speaker search is empty, read a window of an episode they appeared on and say the lines come from their appearance, since caption lines do not name the speaker.
• A chunk found with speakerIds can include other people's lines. Lines start with "Name: " when the speaker is known. Quote only the person's lines. When no line names a speaker, say the attribution covers the passage, not each line.
• Put the user's topic in query for the speaker search (it needs a word or phrase of two or more characters).
• Mentions and momentum count the shows Arcmira indexes, not all media.

After the answer

Monitors can follow what a research turn found, and each alert uses 25 credits from the account's plan, then its on-demand budget. The company-watch skill sets one up.

If anything was wrong, slow, or missing for the user, or they want a source or capability Arcmira lacks, send one arcmira_feedback.