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-competitorgit 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-competitor)<a href="https://agentmods.dev/skills/deeployco/youtube-seo-skills/youtube-seo-competitor"><img src="https://agentmods.dev/badge/skills/deeployco/youtube-seo-skills/youtube-seo-competitor/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-competitor"><img src="https://agentmods.dev/badge/skills/deeployco/youtube-seo-skills/youtube-seo-competitor.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 | $0.00082 | $0.02361 |
| Opus 5 | $0.00041 | $0.01180 |
| Sonnet 5 | $0.00016 | $0.00472 |
| Haiku 4.5 | $0.00008 | $0.00236 |
Grade A, and why
youtube-seo-competitor 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Competitor Intelligence (Advanced)
Competitor analysis on YouTube is about pattern extraction, not imitation. Find what works, understand why, and apply the mechanism (not the surface form) to the user's channel.
Process
1. Identify competitors
If the user provides a competitor list → use it and add 1-2 mid-tier comparables. Otherwise derive competitors using only first-party sources (no third-party analytics tools — they rate-limit or error):
- From target keywords (preferred): call the YouTube Data API
search.list?part=snippet&type=video&order=viewCount&q={keyword}for the user's 3-5 primary keywords. Cluster the results bychannelIdand pick the 5 channels that appear most often in the top 20. - API-less fallback:
yt-dlp "ytsearch20:{keyword}" --flat-playlist -Jreturns the same ranked list with channel IDs — no API key, no scraping. - From the user's channel: read the
relatedPlaylists+featuredChannelsUrls(brandingSettings) via the API. As a secondary signal, pull the top 10 videos' descriptions viafetch_channel.pyand extract@handle/ channel-URL mentions. - Tier balance: include 1-2 channels 5-10× the user's subscriber size (aspiration) and 2-3 within 2-5× (comparable).
If all of the above fail or the API quota is exhausted, ask the user for 3-5 competitor channel URLs directly rather than falling back to third-party scrapers.
2. Collect per-competitor data
For each competitor (use scripts/fetch_channel.py + API where
possible):
- Channel stats: subs, total views, video count, join date, country, topic IDs
- Upload cadence: last 25 videos → median + stddev of days-between
- Format mix: long-form / Shorts / live / premiere percentages
- Top 10 all-time by view count
- Top 10 last-90-days (recent winners — more actionable)
- Bottom 10 last-90-days (what is NOT working — as informative as the winners)
- Median stats: length, views per video, like-to-view ratio, comment velocity
- Playlist structure: count, median size, top playlist view counts
- Community tab activity (last 30 days)
- Thumbnail contact sheet: last 12 thumbnails as a 4x3 grid
- Title corpus: all titles from last 50 uploads
- Caption/transcript for top 5 videos via yt-dlp (used for hook extraction)
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 · 255 lines · 82 tokens per session scan A 99cc41ab2a9f
youtube-seo-competitor is a skill published in the GitHub repository deeployCO/youtube-seo-skills (9 stars, last pushed 5mo ago), licensed MIT. It adds 82 tokens to every session and 2,361 once invoked, about $0.0004 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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