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-title-tag-optimizergit 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-title-tag-optimizer)<a href="https://agentmods.dev/skills/nikhilbhansali/youtube-data-skills/youtube-title-tag-optimizer"><img src="https://agentmods.dev/badge/skills/nikhilbhansali/youtube-data-skills/youtube-title-tag-optimizer/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-title-tag-optimizer"><img src="https://agentmods.dev/badge/skills/nikhilbhansali/youtube-data-skills/youtube-title-tag-optimizer.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 | $0.00126 | $0.01234 |
| Opus 5 | $0.00063 | $0.00617 |
| Sonnet 5 | $0.00025 | $0.00247 |
| Haiku 4.5 | $0.00013 | $0.00123 |
Grade A, and why
youtube-title-tag-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 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
YouTube Title & Tag Optimizer
Optimize your video title, tags, and description before publishing by analyzing what works for top-ranking videos.
Usage
/youtube-title-tag-optimizer "air fryer recipes"
/youtube-title-tag-optimizer "Python tutorial" --my-title "Learn Python in 10 Minutes"
/youtube-title-tag-optimizer --keyword "home workout" --my-title "Best Home Workout for Beginners 2024"
Instructions
Step 1: Parse Arguments
- Keyword (required): the topic/keyword to optimize for
- --my-title "..." (optional): the user's working title to score and improve
Step 2: Get the API Key
Check the user's Claude memory for a YouTube Data API v3 key. If not found, ask:
"I need a YouTube Data API v3 key to analyze ranking videos. You can get one from the Google Cloud Console. Please paste your key."
Step 3: Run the Bundled Script
Run scripts/optimize_title_tags.py — resolve the path relative to this skill's own directory:
YT_API_KEY=API_KEY python3 <skill-dir>/scripts/optimize_title_tags.py "KEYWORD" [--my-title "User Title"]
Dependency: pip3 install google-api-python-client.
The script pulls the top 50 by relevance and top 50 by views, analyzes every title
for length, formatting elements, power words and structures, aggregates tag usage,
and — when --my-title is given — scores it out of 100 with itemized feedback.
Step 4: Read the Data
reports/data/title-tag-<keyword-slug>-<YYYY-MM-DD>.json
Step 5: Write the Report
Write to the path the script printed:
reports/title-tag-<keyword-slug>-<YYYY-MM-DD>.md
# Title & Tag Optimization: [Keyword]
*Analyzed [date] | [N] videos*
## Your Title Score (if provided)
**Score: [X]/100**
| Criterion | Status |
|-----------|--------|
[Feedback items as table rows]
### Improved Title Suggestions
Generate 5-10 optimized title variations based on:
- Top-performing patterns from the data
- Power words that work in this niche
- Optimal length (40-70 chars)
- Keyword placement (front-loaded)
## Keyword Analysis
| Metric | Value |
|--------|-------|
| Videos Analyzed | |
| Avg Title Length | chars / words |
| Avg Tags per Video | |
## Top-Performing Titles
| # | Title | Views | Engagement | Key Patterns |
|---|-------|-------|------------|--------------|
[Top 10 titles with analysis]
## Title Pattern Analysis
### What Works for "[Keyword]"
**Title Structures:**
| Structure | Usage % | Avg Views |
|-----------|---------|-----------|
Use `aggregate_title_analysis.structures` and `structure_avg_views`.
**Power Words:**
| Category | Usage % | Top Words |
|----------|---------|-----------|
**Formatting Elements:**
| Element | Usage % | Impact |
|---------|---------|--------|
[numbers, brackets, caps, emoji, year, etc.]
### Winning Title Formulas
Based on top performers, these formulas work best for this keyword:
1. [Formula 1 with example]
2. [Formula 2 with example]
3. [Formula 3 with example]
## Optimized Tag Set
Ordered by priority:
### Primary Tags (use these first)
[Top 10 most-used tags]
### Secondary Tags
[Next 10 tags]
### Long-tail Tags
[Suggested long-tail variations]
### Recommended Tag Set (copy-paste ready)
[Comma-separated complete tag set optimized for the keyword]
## Description SEO Template
Based on top performers' first lines:
[Template with placeholders]
### Top Description First Lines
| Video | First Line |
|-------|------------|
## Hashtag Recommendations
Top hashtags to use based on video data.
## Quick-Reference Checklist
- [ ] Title is 40-70 characters
- [ ] Keyword appears in first 5 words
- [ ] Contains a number or power word
- [ ] Uses proven structure (how-to/listicle/question)
- [ ] Tags include primary + secondary + long-tail
- [ ] Description first line contains keyword
## 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 · 168 lines · 126 tokens per session scan A 72e3e1a1274c
youtube-title-tag-optimizer is a skill published in the GitHub repository nikhilbhansali/youtube-data-skills (2 stars, last pushed 25d ago), licensed MIT. It adds 126 tokens to every session and 1,234 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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