AI Marketing Skills is a collection of open-source workflows that help AI coding agents handle marketing and sales work, including growth experiments, pipeline management, content operations, outbound outreach, SEO, and finance analysis. It is intended for marketing and sales teams that want reusable agent-driven processes. The catalogue entries package these workflows as skills for compatible coding agents.
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 ericosiu/ai-marketing-skills --skill yt-competitive-analysisgit clone --depth 1 https://github.com/ericosiu/ai-marketing-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/ericosiu/ai-marketing-skills/yt-competitive-analysis)<a href="https://agentmods.dev/skills/ericosiu/ai-marketing-skills/yt-competitive-analysis"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/yt-competitive-analysis/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/ericosiu/ai-marketing-skills/yt-competitive-analysis"><img src="https://agentmods.dev/badge/skills/ericosiu/ai-marketing-skills/yt-competitive-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.00768 |
| Opus 5 | $0.00026 | $0.00384 |
| Sonnet 5 | $0.00010 | $0.00154 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
yt-competitive-analysis 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Competitive Analysis
Outlier detection and packaging pattern extraction for YouTube channels.
When to Use
- User asks for YouTube competitive analysis
- User wants to find viral video patterns
- User wants packaging/title inspiration from specific creators
- User wants to track competitor YouTube performance
Prerequisites
- YouTube Data API v3 key set as
$YOUTUBE_API_KEY
Usage
# Analyze specific channels
python3 analyze.py "$YOUTUBE_API_KEY" --channels "@handle1,@handle2" --days 30
# Use predefined sets
python3 analyze.py "$YOUTUBE_API_KEY" --set ai
python3 analyze.py "$YOUTUBE_API_KEY" --set business
python3 analyze.py "$YOUTUBE_API_KEY" --set both
# Export formats
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output json
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output console
Predefined Channel Sets
AI Creators: Jeff Su, Alex Finn, Riley Brown, Dan Martell, Matt Wolfe, Nate Herk, Grace Leung, Matt Berman
Business Creators: Alex Hormozi, Gary Vaynerchuk, Patrick Bet-David, Codie Sanchez, Leila Hormozi, Iman Gadzhi, My First Million
Output Interpretation
- Multiplier: Times above channel average (2.0x = double normal)
- Outlier threshold: 2x average. Study anything above this.
- Title patterns: Common words in outlier titles indicate proven formats
- Cadence: Videos per week. Higher cadence creators may have lower per-video averages.
Channel Analytics Feedback Loop
Competitive analysis is only half the loop. When you have access to the channel's own analytics, compare candidate packaging against actual performance after publish.
Before recommending a package:
- Pull channel baseline by topic, title pattern, thumbnail pattern, length, publish day/time, and format.
- Check impressions, CTR, average view duration, retention curve, watch time, subscribers gained, comments, and traffic source.
- Compare the proposed title/thumbnail/hook against similar historical videos and competitor outliers.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 88 lines · 52 tokens per session scan A da7d4d51eb76
yt-competitive-analysis is a skill published in the GitHub repository ericosiu/ai-marketing-skills (3,521 stars, last pushed 4d ago), licensed MIT. It adds 52 tokens to every session and 768 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 2026-08-30.
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