Borrowing it
Nothing to install: this file belongs to different-ai/agent-bank. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/different-ai/agent-bank/main/.opencode/skill/youtube-rl-tracker/SKILL.mdgit clone --depth 1 https://github.com/different-ai/agent-bankWrote 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/different-ai/agent-bank/youtube-rl-tracker)<a href="https://agentmods.dev/skills/different-ai/agent-bank/youtube-rl-tracker"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/youtube-rl-tracker/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/different-ai/agent-bank/youtube-rl-tracker"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/youtube-rl-tracker.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 | $0.00028 | $0.01447 |
| Opus 5 | $0.00014 | $0.00724 |
| Sonnet 5 | $0.00006 | $0.00289 |
| Haiku 4.5 | $0.00003 | $0.00145 |
Grade A, and why
youtube-rl-tracker 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 10d 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What I Do
Track YouTube video performance to discover patterns in what works. This is "poor man's reinforcement learning" - manually logging outcomes to improve over time.
The RL Loop
1. PUBLISH -> Upload video with hypothesis (thumbnail style, title hook, topic)
2. WAIT -> Let it run for 48-72 hours
3. LOG -> Record in Notion with views, CTR, retention
4. ANALYZE -> Compare winners vs losers
5. REPEAT -> Apply learnings to next video
Key Insight from First Data Point
Video 1: "Using AI agents to pay bills and send invoices"
- 8 views in 1 day
- Plain talking head thumbnail
- Generic title
Video 2: "Paying My Contractor Through Claude | AI-Powered Finance"
- 133 views in 5 days (16x better!)
- Thumbnail shows: Face + Product UI overlay + Text "I Let AI Pay My Bills"
- Title has: Specific action + Brand name (Claude) + Category tag
What Made Video 2 Win:
- Thumbnail has TEXT overlay - "I Let AI Pay My Bills" creates curiosity
- Shows the PRODUCT - UI screenshot proves it's real, not just talk
- Face + Context - Person looking at the UI, not just talking
- Specific title - "Paying My Contractor" > "pay bills" (concrete vs abstract)
- Brand name in title - "Claude" attracts AI-interested audience
- Category tag - "AI-Powered Finance" helps discoverability
Hypothesis to Test:
Thumbnails with TEXT + PRODUCT UI + FACE outperform plain talking head thumbnails by 10x+
Database Schema
Core Fields (Outcomes)
| Property | Type | Purpose |
|---|---|---|
| Title | title | Video title |
| Views | number | Total views |
| CTR | number | Click-through rate (%) |
| Retention | number | Average view duration (%) |
| Days Live | number | Days since publish |
| Views/Day | formula | Views / Days Live |
| Worked? | checkbox | Binary gut-check - was this a win? |
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.
- 10d ago First seen · 162 lines · 28 tokens per session scan A a6a33644c5aa
youtube-rl-tracker is a skill published in the GitHub repository different-ai/agent-bank (249 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 1,447 once invoked, about $0.0001 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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