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Youtube

Organization
unifapi-agent
youtube

When a workflow needs public YouTube data through UnifAPI — channel, video, and playlist metadata, subscriber/view/like counts, video comments, caption tracks, channel community posts, related and trending videos, hashtag videos, and search across videos, channels, and playlists. Also use on "who owns this topic on YouTube," "pull this channel's videos," "YouTube view counts," "what are commenters saying," "get this video's captions/transcript," "this channel's playlists," or when another skill (creator vetting, competitor reception, content demand) needs the deterministic YouTube read path. Connect via the `unifapi` skill first. Read-only research, never uploads.

Overview

Publisherunifapi-agent
Repositoryagents
Skill nameyoutube
Stars
590
Forks
115
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by unifapi-agent on GitHub. Read the source before you install it.

Installation

Install the Youtube AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/unifapi-agent/agents/tree/main/skills/youtube-agent/youtube
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/unifapi-agent/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/youtube-agent/youtube .claude/skills/youtube
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Youtube in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Youtube on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Youtube is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

youtube

The deterministic read path for public YouTube data through UnifAPI. This is a Data Skill: it does not run a marketing job on its own — it names the concrete youtube/... operations, response shapes, and the real coverage limits so any channel- or content-first workflow (creator vetting, competitor reception, content demand) reads from one known recipe instead of rediscovering the surface each time.

Read-only — eyes, not hands. It researches public YouTube data and returns cited records; it never uploads, comments, or subscribes, and UnifAPI never holds YouTube credentials.

Use the unifapi skill for live evidence

Connect once through the shared unifapi skill (OAuth MCP), then call the operations below. Channels are keyed by {channel_id} and videos by {video_id} — resolve a handle or URL to a channel id first (see Gotchas). Keep any billing metadata so the output can state record cost.

Response contract

Single-entity endpoints return the object in data:

json
{
  "request_id": "unif_...",
  "data": {},
  "billing": { "records_charged": 1, "balance_remaining": 99 }
}

List endpoints return an array in data plus pagination:

json
{
  "request_id": "unif_...",
  "data": [],
  "pagination": { "has_more": false, "next_cursor": null },
  "billing": { "records_charged": 1 }
}

When pagination.has_more is true, pass pagination.next_cursor as the next request's cursor. Always preserve billing when reporting cost.

Core operations

NeedOperation
Search videosyoutube/search (?q=..., optional sort_by/upload_date/duration/region/language)
Search channels / playlistsyoutube/search/channels · youtube/search/playlists (?q=...)
Trendingyoutube/trending (optional type = now/music/gaming/movies)
Hashtag videosyoutube/hashtags/{tag}/videos
Resolve handle/URL → idyoutube/resolve/channel-id (?url=...)
Channel pageyoutube/channels/{channel_id}
Channel videos / shortsyoutube/channels/{channel_id}/videos · .../shorts
Channel playlistsyoutube/channels/{channel_id}/playlists
Channel community postsyoutube/channels/{channel_id}/community
Search within a channelyoutube/channels/{channel_id}/search (?q=...)
Video metadatayoutube/videos/{video_id}
Related videosyoutube/videos/{video_id}/related
Video commentsyoutube/videos/{video_id}/comments (optional sort_by = top/newest)
Video captions / transcriptyoutube/videos/{video_id}/captions
Playlist page / videosyoutube/playlists/{playlist_id} · .../videos

Need a field not listed here? Use the unifapi skill's get_operation to read the exact schema before calling — but pick the operation from this table, don't discover blind.

Workflow

The deterministic recipes. Pick the one that matches the job; each names exactly what to call.

  1. Frame the query. Call youtube/search?q=... for who owns a topic, or youtube/trending for what's surging now.
  2. Resolve a channel. Call youtube/resolve/channel-id?url=... to get the channel_id, then youtube/channels/{channel_id} for subscriber_count, video_count, and view_count.
  3. Read a channel's output. Call youtube/channels/{channel_id}/videos (and .../shorts); each video's view_count is the demand signal. Page via next_cursor. youtube/channels/{channel_id}/playlists maps how the catalog is organized; youtube/channels/{channel_id}/community reads announcements and engagement outside videos; youtube/channels/{channel_id}/search?q=... finds a creator's coverage of one topic without scanning the whole catalog.
  4. Read a video and its neighborhood. Call youtube/videos/{video_id} for metadata (now including like_count, category, published_at, has_captions), then youtube/videos/{video_id}/related to map adjacent demand and competing content.
  5. Read audience and transcript. Call youtube/videos/{video_id}/comments (sort_by top/newest) for what the audience says — raw comment text, like and reply counts; any sentiment or FAQ grouping is yours to compute, the API returns text only. Call youtube/videos/{video_id}/captions for caption tracks (timed-text url per language) to read or analyze the transcript.
  6. Walk a playlist or hashtag. Call youtube/playlists/{playlist_id} then .../videos to ingest a curated series in order; youtube/hashtags/{tag}/videos tracks a campaign or trend across creators.
  7. Size topic demand. Aggregate view_count across the search and related results for a topic; title, description, and keywords show how the topic is framed.
  8. Cite everything. Every figure ties back to the video or channel it came from; report billing.records_charged (or estimate when billing metadata is absent).

Shape notes

  • YouTubeChannel — keyed by {channel_id}. subscriber_count, video_count, view_count, handle, keywords, links, country, created_at. is_verified is not populated by the upstream and is always false — don't report verification you can't read.
  • YouTubeVideo — keyed by {video_id}. view_count, like_count (best-effort; 0 when the creator hides it), category, channel_id, author, duration_seconds, keywords, published_at, has_captions, is_live, is_private.
  • YouTubeVideoPreview — search/related/list rows: adds published_time. category is part of the shape but empty on list rows (only YouTubeVideo detail carries it).
  • YouTubeComment — text, author, author_channel_id, like_count, reply_count, published_time, is_verified, is_creator. Text only — no sentiment field.
  • YouTubeCaptions — video_id, tracks[] (language_code, timed-text url, is_translatable), translation_languages[]. Single object, billed as one record. Track urls are time-limited.
  • YouTubePlaylist / YouTubePlaylistPreview — playlist title, channel_id, author, video_count (detail adds view_count, last_updated; preview adds first_video_id).
  • YouTubeChannelPreview — search-channels rows: title, handle, description, subscriber_count, video_count.
  • YouTubeCommunityPost — text, author, vote_count, reply_count, attachment_type, image_url.

Gotchas

  • Resolve only goes to channel ids. youtube/resolve/channel-id?url=... resolves a handle or URL to a {channel_id} — call it before any youtube/channels/{channel_id}/... call. There is no video- or playlist-id resolver; pass a known {video_id} / {playlist_id} (parse it from the URL yourself).
  • Comments are raw text. youtube/videos/{video_id}/comments returns comment text plus like/reply counts — no sentiment, no FAQ grouping. Compute those yourself from the text; don't claim a sentiment field the API doesn't return.
  • like_count is best-effort. YouTubeVideo.like_count is 0 when the creator hides likes — treat 0 as "hidden or none," not authoritative.
  • is_verified is always false for channels (upstream doesn't expose it) — don't report channel verification.
  • Captions can be empty. A video with captions disabled returns tracks: []; check before promising a transcript. Track urls are time-limited.
  • Hashtags take the tag with or without a leading # (youtube/hashtags/lofi/videos).
  • A low balance can silently truncate list pages: check billing.truncated_due_to_balance — when true the page is partial.

Output

Return the records the calling workflow needs, each cited to its video or channel, plus a one-line cost note (records_charged). When this skill is used directly, a compact demand brief is the default:

markdown
**{Channel}** — {subscribers} subscribers, {videos} videos, {totalViews} views. Top videos for {topic}: {title} ({views} views). Related/competitors: {channels}. Evidence: {URLs}. Records: ~{N}.

Related skills

  • kol-pricing, creator-shortlist, audience-fit-check (Influencer Marketing) — price and vet YouTube channels on top of this read path.
  • competitor-profiling (Competitive Intelligence), content-opportunity-brief (Content Strategy) — reception and content-demand work that reads YouTube first.
  • unifapi — the shared data skill: connect MCP and look up exact schemas with get_operation.

Frequently asked questions

What does the Youtube AI skill do?

When a workflow needs public YouTube data through UnifAPI — channel, video, and playlist metadata, subscriber/view/like counts, video comments, caption tracks, channel community posts, related and trending videos, hashtag videos, and search across videos, channels, and playlists. Also use on "who owns this topic on YouTube," "pull this channel's videos," "YouTube view counts," "what are commenters saying," "get this video's captions/transcript," "this channel's playlists," or when another skill (creator vetting, competitor reception, content demand) needs the deterministic YouTube read path...

Why use Youtube on TypingMind?

Because you install it once and use it with any model. Youtube is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Youtube in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/unifapi-agent/agents/tree/main/skills/youtube-agent/youtube. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Youtube?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Youtube?

As many as you like. As long as a model supports skills, you can use Youtube with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Youtube AI skill free?

Yes. It is published on GitHub by unifapi-agent under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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