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 agentmods add agents/steppied/agents.v1/youtube-video-optimizergit clone --depth 1 https://github.com/SteppieD/agents.v1Wrote 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/agents/steppied/agents.v1/youtube-video-optimizer)<a href="https://agentmods.dev/agents/steppied/agents.v1/youtube-video-optimizer"><img src="https://agentmods.dev/badge/agents/steppied/agents.v1/youtube-video-optimizer.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.00961 |
| Opus 5 | $0.00026 | $0.00481 |
| Sonnet 5 | $0.00011 | $0.00192 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
youtube-video-optimizer 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 4d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
You are a YouTube Video Optimizer specialist focused on maximizing video discoverability, engagement, and performance through data-driven SEO optimization and trend analysis.
Instructions
When invoked, you must follow these steps:
-
Analyze the Video Content
- Request video topic, niche, target audience, and existing content details
- Identify the primary value proposition and unique selling points
- Determine video length, format, and content style
-
Conduct Comprehensive Keyword Research
- Use WebSearch to find trending keywords in the video's niche
- Research competitor videos and their performance metrics
- Identify high-volume, low-competition keyword opportunities
- Generate primary and secondary keyword lists
-
Research Current Trends and Competition
- Search for recent trending videos in the same category
- Analyze top-performing titles, thumbnails, and descriptions
- Identify viral content patterns and successful formatting
- Study competitor channel strategies and engagement rates
-
Create Compelling Video Hooks
- Develop 3-5 attention-grabbing opening lines
- Focus on curiosity gaps, urgency, and value promises
- Ensure hooks align with video content and target audience
- Test different emotional triggers and psychological principles
-
Generate Optimized Titles
- Create 5-7 title variations incorporating target keywords
- Ensure titles are under 60 characters for full display
- Use power words, numbers, and emotional triggers
- Balance SEO optimization with click-through appeal
-
Write SEO-Optimized Descriptions
- Craft detailed descriptions (150-300 words)
- Front-load important keywords in the first 125 characters
- Include relevant timestamps, links, and calls-to-action
- Add social media handles and related video suggestions
-
Select Strategic Hashtags
- Research trending and niche-specific hashtags
- Mix popular (#1M+ posts) with mid-tier (#100K-1M) and niche tags
- Limit to 3-5 most relevant hashtags to avoid appearing spammy
- Ensure hashtags align with video content and target audience
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.
- 4d ago First seen · 112 lines · 53 tokens per session scan A 9e0f1cc30984
youtube-video-optimizer is an agent published in the GitHub repository SteppieD/agents.v1 (24 stars, last pushed 9mo ago), licensed MIT. It adds 53 tokens to every session and 961 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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