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 nikhilbhansali/youtube-data-skills --skill youtube-trending-scannergit clone --depth 1 https://github.com/nikhilbhansali/youtube-data-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/nikhilbhansali/youtube-data-skills/youtube-trending-scanner)<a href="https://agentmods.dev/skills/nikhilbhansali/youtube-data-skills/youtube-trending-scanner"><img src="https://agentmods.dev/badge/skills/nikhilbhansali/youtube-data-skills/youtube-trending-scanner/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/nikhilbhansali/youtube-data-skills/youtube-trending-scanner"><img src="https://agentmods.dev/badge/skills/nikhilbhansali/youtube-data-skills/youtube-trending-scanner.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 | $0.00114 | $0.00979 |
| Opus 5 | $0.00057 | $0.00490 |
| Sonnet 5 | $0.00023 | $0.00196 |
| Haiku 4.5 | $0.00011 | $0.00098 |
Grade A, and why
youtube-trending-scanner 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Trending Scanner
Scan what's trending right now in any YouTube niche -- find breakout videos, rising channels, and emerging topics.
Usage
/youtube-trending-scanner "meditation"
/youtube-trending-scanner "AI tools" --days 14
/youtube-trending-scanner "home cooking" --days 30
Instructions
Step 1: Parse Arguments
- Niche/keyword (required): the niche to scan
- --days N (optional): time window to scan (default: 14, max: 30)
Step 2: Get the API Key
Check Claude memory for a YouTube Data API v3 key. If not found, ask:
"I need a YouTube Data API v3 key. You can get one from the Google Cloud Console. Please paste your key."
Step 3: Run the Bundled Script
Run scripts/scan_trending.py — resolve the path relative to this skill's own directory:
YT_API_KEY=API_KEY python3 <skill-dir>/scripts/scan_trending.py "NICHE" [--days N]
Dependency: pip3 install google-api-python-client.
The script searches the window three ways (relevance, viewCount, date), pulls a 90-day baseline for comparison, then computes velocity outliers, rising channels, trending words/bigrams, format distribution, and publishing-rate change.
Step 4: Read the Data
reports/data/trending-scan-<niche-slug>-<YYYY-MM-DD>.json
Step 5: Write the Report
Write to the path the script printed:
reports/trending-scan-<niche-slug>-<YYYY-MM-DD>.md
# Trending Scanner: [Niche]
*Scanned [date] | Last [N] days | [N] videos analyzed*
## Hot Right Now
Overall trend assessment: Is this niche heating up, stable, or cooling down?
Compare recent publishing rate vs baseline.
## Breakout Videos (Velocity Outliers)
| # | Title | Views | Velocity (views/day) | Channel | Channel Size | Age |
|---|-------|-------|---------------------|---------|--------------|-----|
These videos are getting disproportionate views. What do they have in common?
## Trending Topics
Words and phrases appearing frequently in recent high-performing content.
Topic clusters and emerging themes.
## Rising Channels
Small channels getting unusual traction right now.
| Channel | Subs | Recent Videos | Recent Views | Avg View/Sub Ratio |
|---------|------|---------------|--------------|-------------------|
## Format Trends
What formats are being used? Which are performing best?
Shorts vs long-form breakdown.
## Content Velocity
- Current niche publishing rate vs baseline
- Is competition increasing or decreasing?
- Saturation signals
## Timely Content Recommendations
3-5 specific video ideas based on current trends:
- What to make THIS WEEK
- Why (data backing)
- Format and angle recommendation
## Trend Assessment
- Growing / Stable / Declining
- First-mover opportunities
- Risks and considerations
## Quota Usage
| Operation | Calls | Units |
|-----------|-------|-------|
Use the `quota_used.breakdown` block from the JSON.
What ships with it
2 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 · 123 lines · 114 tokens per session scan A f91be85dbfe8
youtube-trending-scanner is a skill published in the GitHub repository nikhilbhansali/youtube-data-skills (2 stars, last pushed 25d ago), licensed MIT. It adds 114 tokens to every session and 979 once invoked, about $0.0006 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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