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 deeployCO/youtube-seo-skills --skill youtube-seo-keywordsgit clone --depth 1 https://github.com/deeployCO/youtube-seo-skillsWrote 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/deeployco/youtube-seo-skills/youtube-seo-keywords)<a href="https://agentmods.dev/skills/deeployco/youtube-seo-skills/youtube-seo-keywords"><img src="https://agentmods.dev/badge/skills/deeployco/youtube-seo-skills/youtube-seo-keywords/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/deeployco/youtube-seo-skills/youtube-seo-keywords"><img src="https://agentmods.dev/badge/skills/deeployco/youtube-seo-skills/youtube-seo-keywords.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 | $0.00070 | $0.02316 |
| Opus 5 | $0.00035 | $0.01158 |
| Sonnet 5 | $0.00014 | $0.00463 |
| Haiku 4.5 | $0.00007 | $0.00232 |
Grade A, and why
youtube-seo-keywords 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Keyword & Topic Research (Advanced)
Process
- Capture seed: keyword, channel URL, or niche description. If only
a channel URL is provided, extract 5-10 seed topics from:
- The channel's top 25 videos by views (title + description entities)
- The channel's topic IDs from
channels.list?part=topicDetails
- Expand the seed set to 100-300 candidate keywords from
first-party sources only (third-party tools like VidIQ,
TubeBuddy, Ahrefs, and SocialBlade rate-limit and generate errors —
avoid them):
- YouTube suggest API (
https://suggestqueries.google.com/complete/ search?client=firefox&ds=yt&q={seed}) — no key, returns JSON, iterate by appendinga-zto the seed for long-tail expansion - YouTube Data API
search.list?part=snippet&q={seed}&type=video— surface the top 50 ranking titles and mine their titles/tags for co-occurring terms - yt-dlp
ytsearch50:{seed}as an API-less equivalent - Google autocomplete (
suggestqueries.google.com/complete/search? client=firefox&q={seed}) for parent-topic discovery - User-provided seed list or topic outline
- YouTube suggest API (
- Classify intent for each keyword:
- Informational ("what is ...", "how does ... work")
- Tutorial ("how to ...", "... tutorial", "step by step")
- Commercial / Review ("... review", "best ...", "... vs ...")
- Entertainment (celebrity names, event names, memes)
- News / Topical (breaking or time-sensitive)
- Navigational (brand or channel names) → Each intent class maps to a different video format and surface.
- Classify primary surface:
- Search-dominant: "how to", "tutorial", long-tail specific queries
- Browse-dominant: broad topics, personalities, trending
- Suggested-dominant: sits adjacent to an existing popular video
- Entity mapping: for each keyword, identify the Knowledge Graph entities involved (people, brands, products, concepts). Group keywords that share entities into topic clusters.
- Competition analysis per keyword (top 10 SERP):
- Median subscriber count of ranking channels
- Median video age (weeks)
- Median view count
- Presence of mega channels (>1M subs) in top 3
- Thumbnail/title quality median (estimate)
- SERP features (Shorts shelf, mix shelf, promoted result)
- Seasonality: pull Google Trends 5-year curve per keyword cluster.
Classify:
- Evergreen: flat or slowly rising
- Seasonal: predictable annual peak (gift guides, tax season)
- Trending: fast-rising, <6 months of data
- Decaying: fast-dropping
- Opportunity scoring (refined formula below)
- Cluster keywords into 3-6 topic buckets with a pillar + satellites shape optimized for topical authority
- Content calendar (optional) with pillar cadence and reactive slots
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 · 209 lines · 70 tokens per session scan A 9b97d9e70fae
youtube-seo-keywords is a skill published in the GitHub repository deeployCO/youtube-seo-skills (9 stars, last pushed 5mo ago), licensed MIT. It adds 70 tokens to every session and 2,316 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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