Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add tubealfred/mcp --skill youtube-discovery-researchgit clone --depth 1 https://github.com/tubealfred/mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tubealfred/mcp/youtube-discovery-research)<a href="https://agentmods.dev/skills/tubealfred/mcp/youtube-discovery-research"><img src="https://agentmods.dev/badge/skills/tubealfred/mcp/youtube-discovery-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tubealfred/mcp/youtube-discovery-research"><img src="https://agentmods.dev/badge/skills/tubealfred/mcp/youtube-discovery-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.00526 |
| Opus 5 | $0.00025 | $0.00263 |
| Sonnet 5 | $0.00010 | $0.00105 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
youtube-discovery-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube discovery research with TubeAlfred
Use the hosted read-only MCP server at https://mcp.tubealfred.com/. TubeAlfred provides public YouTube discovery data without changing YouTube accounts or requiring the user's Google YouTube Data API quota.
Select an entry point
- Use
youtube_search_queryfor a natural-language topic or named competitor. - Use
youtube_search_hashtagfor a hashtag-specific question. - Use
youtube_trending_videosoryoutube_trending_shortsonly when the user asks what is currently trending. - Use
youtube_search_suggestionsto inspect query phrasing and adjacent audience language. - Use
youtube_url_resolvewhen the supplied URL could represent a video, channel, playlist, or another YouTube resource. - Use playlist and channel tools after discovery to inspect a selected result in depth.
Expand deliberately
Search and related list operations can return continuation tokens. Fetch another page only when the requested result count, diversity, or confidence requires it. For large comparisons, collect identifiers first and use youtube_videos_batch or youtube_channels_batch rather than issuing avoidable one-by-one calls. Batch calls cost credits per successfully resolved identifier.
Research method
- Record the query, hashtag, region, or trend surface used.
- Build a candidate set before enriching individual results.
- Deduplicate repeated videos, channels, and playlists.
- Compare public metadata on a consistent basis such as publication time, topic, duration, channel, and available engagement fields.
- Use transcripts or comments only for shortlisted results and only when the user's question needs that evidence.
- Distinguish search ranking, recommendation adjacency, and trending placement; they are different discovery signals.
Boundaries
Search results are time-, region-, and upstream-dependent. Do not present a snapshot as permanent ranking truth. Do not infer private keyword volume, impressions, click-through rate, viewer cohorts, or causality from public discovery data. Respect Retry-After, stop on permission or unavailable-data outcomes, and avoid repeating successful calls because they can consume credits.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 39 lines · 50 tokens per session scan A 158ccea523be
youtube-discovery-research is a skill published in the GitHub repository tubealfred/mcp (2 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 526 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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