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YouTube Data Skill

The YouTube Data Skill is the source-sliced data guide a Platform Agent owns: it names the concrete `youtube/...` operations, response shapes, and the real coverage limits (no comment listing) for reading public YouTube data through UnifAPI MCP, so any channel or content research (creator vetting, competitor reception, content demand) runs from one deterministic workflow. Read-only — it never uploads or comments.

Agent-native

Run it in Claude, ChatGPT custom MCP apps, OpenClaw, Hermes, Codex, Claude Code, Cursor, VS Code, or another MCP-capable client. No dedicated GUI flow and no separate LLM API key.

Backed by live public data

Every step is grounded in live public-data records UnifAPI returns, so the output cites what is actually ranking, posting, or being said — not a generic best-practice list.

Composable & open source

Skills cross-reference each other and live in a public, MIT-style repo. Read the full SKILL.md on GitHub, fork it, or run it as-is inside your agent.

Run prompt

Paste this into Codex or Claude Code

The prompt is intentionally editable. Replace the handles, market, budget, and campaign goal, then let the agent call UnifAPI MCP when it needs live public data.

Using the YouTube Data Skill, research who owns the topic "AI coding tools": search videos and channels, read their subscriber and view counts, and pull related videos for demand. Return a cited brief on the strongest channels. Read-only.
How the skill works

The full skill, rendered from its SKILL.md

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:

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

List endpoints return an array in data plus pagination:

{
  "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 videos/channelsyoutube/search (?q=...)
Trendingyoutube/trending
Resolve handle/URL → idyoutube/resolve/channel-id (?url=...)
Channel pageyoutube/channels/{channel_id}
Channel videos / shortsyoutube/channels/{channel_id}/videos · .../shorts
Video metadatayoutube/videos/{video_id}
Related videosyoutube/videos/{video_id}/related

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.
  4. Read a video and its neighborhood. Call youtube/videos/{video_id} for metadata, then youtube/videos/{video_id}/related to map adjacent demand and competing content.
  5. 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.
  6. 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, is_verified, country, created_at.
  • YouTubeVideo — keyed by {video_id}. view_count, channel_id, author, duration_seconds, keywords, is_live, is_private. No per-video like_count is exposed — use view_count.
  • YouTubeVideoPreview — search/related rows: adds published_time and category.

Gotchas

  • No comment listing. YouTube exposes channel and video metadata, view counts, related videos, search, and trending — but no comments. The demand signal is view_count plus titles, descriptions, and keywords.
  • Videos expose view_count, not a per-video like_count — don't report likes you can't read.
  • Resolve a handle or URL with youtube/resolve/channel-id?url=... before any youtube/channels/{channel_id}/... call.
  • 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:

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

Source: youtube/SKILL.md on GitHub — open a PR there to improve it.

Public-data tools

The live APIs this skill calls

Every operation the skill names is one of these UnifAPI platforms — still visible and callable for product code, debugging, and custom agent flows.

  • The concrete `youtube/...` operations for each common channel/video read job
  • Response-contract and shape notes for channels and videos (view counts)
  • Deterministic recipes: resolve channels, read demand, find related content
  • Platform gotchas (no comment listing, view_count signal, cursor pagination)