FI

Retrieve exact spoken quotes with timestamps and speaker details from video and audio transcripts.

Install

mkdir -p .claude/skills/find-quotes && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/19565" && unzip -o skill.zip -d .claude/skills/find-quotes && rm skill.zip

Installs to .claude/skills/find-quotes

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Finds exact spoken quotes and clip-ready moments on podcasts and YouTube: verbatim words, speaker, date, a timestamped link, clip start and end. Uses arcmira.
158 charsno explicit “when” trigger
Intermediate

Key capabilities

  • →Locate exact spoken quotes in media
  • →Retrieve timestamped links for specific moments
  • →Identify speakers within transcript passages
  • →Generate clip boundaries for media segments
  • →Verify spoken content for citation

How it works

The skill uses search to find relevant passages and transcript to extract exact lines with second offsets for precise clipping.

Inputs & outputs

You give it
Quote query and speaker name
You get back
Verbatim text, timestamped URL, and clip boundaries

When to use find-quotes

  • →Find the exact timestamp where a person discussed a specific topic
  • →Retrieve a quote for a blog post or documentation
  • →Create clips from specific interview moments
  • →Verify what a speaker said during a controversial segment
  • →Gather evidence of a specific company mention

About this skill

Find quotes and clip moments

search finds the passage; transcript with start and end returns the exact lines around it with second offsets, which give the verbatim quote and the clip boundaries.

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

When to use

  • find a quote, with the source and a timestamp
  • the moment a show talked about a topic, to clip or cite
  • what a specific person said about a topic, in their words

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.

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

For this task:

  • A speaker resolves with { type: "person" }; pass the id as speakerIds (who said it). A person or brand the passage is about goes in about. A show goes in channelIds as its UC id.
  • People are often named by first name ("Chamath"). Pass what the user said about them as context; resolve suggests the person when one stands out and asks when several fit.
  • The topic words go in query, never in about. If a filtered search returns no chunks, rerun it with fewer filters before saying nothing was found.

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().

What one person said about a topic, with clip boundaries

const NAME = "Chamath", 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 hits = await arcmira.search({ query: "Anthropic IPO", speakerIds: [id], limit: 5 });
const moments = [];
for (const c of hits.chunks.slice(0, 2)) {
  const moment = { episode: c.video_title, show: c.channel_name, date: c.published_at, url: c.watch_url, speakers: c.speakers_by.map(s => s.name) };
  try {
    const t = await arcmira.transcript(c.video_id, { start: Math.max(0, c.start_seconds - 10), end: c.start_seconds + 50 });
    moment.clip = { start: t.lines[0]?.start ?? c.start_seconds, end: t.lines.at(-1)?.end ?? c.start_seconds + 60 };
    moment.lines = t.lines.map(l => `[${Math.round(l.start)}s] ${l.text}`);
  } catch (err) {
    moment.clip = { start: c.start_seconds, end: c.start_seconds + 60 };
    moment.passage = c.text;
    moment.transcript = err.code;
  }
  moments.push(moment);
}
return { speaker: { id, name: e?.name ?? null, assumed, why }, as_of: hits.as_of, ...(moments.length ? { moments } : { none: hits.note ?? null }) };

A good answer

  • Names the speaker or show it searched, with the id, and quotes the words exactly as the transcript lines give them, trimmed to whole sentences, never paraphrased inside quotation marks.
  • Gives the show, the episode title, the date, and the speaker when the chunk or a premium transcript names one.
  • Links the watch_url, which starts at the moment, and gives a clip start and end in seconds from the transcript lines.
  • Says so when nothing matched, with as_of, instead of offering a quote from memory.

Traps

  • search ranks by the words in query; put the distinctive words of the phrase there, not a paraphrase.
  • A chunk found with speakerIds can include other people's lines. Lines start with "Name: " when the speaker is known. Quote only the speaker's lines ("Chamath Palihapitiya: ..."), never the whole passage. When no line names a speaker, the passage still matched that person; say the attribution covers the passage, not each line.
  • Caption lines are machine text: fix nothing inside the quotation marks. A premium transcript (quality: "premium", paid plans) adds speaker ids into speakers[].
  • A caption read uses 4 credits per started 15 minutes of its start/end window, and of the whole episode without one. A Premium read uses credits for the whole video whatever the window.
  • Some videos have no readable caption track yet (transcript_unavailable, transcript_fetching); quote the search chunk text for those and say the words come from the search passage.

Docs: https://arcmira.com/docs/mcp-server

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.

When not to use it

  • →When the video has no readable caption track
  • →When the speaker cannot be resolved
  • →When the query is too broad to return specific chunks

Prerequisites

Access to Arcmira transcript data

Limitations

  • →Caption lines are machine-generated text
  • →Some videos may have unavailable transcripts
  • →Speaker attribution may be limited in non-premium transcripts

How it compares

It provides verbatim transcript lines and specific timestamped URLs for clips, avoiding the need to manually scrub through video content.

Compared to similar skills

find-quotes side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
find-quotes (this skill)07dNo flagsIntermediate
qmd265moReviewIntermediate
biorxiv-database712moReviewBeginner
read-arxiv-paper167moNo flagsIntermediate

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