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 TechNickAI/ai-coding-config --skill youtube-transcript-analyzergit clone --depth 1 https://github.com/TechNickAI/ai-coding-configWrote 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/technickai/ai-coding-config/youtube-transcript-analyzer)<a href="https://agentmods.dev/skills/technickai/ai-coding-config/youtube-transcript-analyzer"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/youtube-transcript-analyzer/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/technickai/ai-coding-config/youtube-transcript-analyzer"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/youtube-transcript-analyzer.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 | $0.00969 |
| Opus 5.5 | $0.00011 | $0.00388 |
| Sonnet 5.5 | $0.00006 | $0.00194 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
youtube-transcript-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 25d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
# Install via pip
pip install yt-dlp
# Or via homebrew (macOS)
brew install yt-dlp
# Verify installation
yt-dlp --version
# Create temporary directory for this analysis
ANALYSIS_DIR=$(mktemp -d)
echo "Using temporary directory: $ANALYSIS_DIR"
Download transcript using yt-dlp to extract subtitles/transcripts to the temporary directory:
# Download transcript only (no video)
yt-dlp --skip-download --write-auto-sub --sub-format vtt --output "$ANALYSIS_DIR/transcript.%(ext)s" URL
# Or get manually created subtitles if available (higher quality)
yt-dlp --skip-download --write-sub --sub-lang en --sub-format vtt --output "$ANALYSIS_DIR/transcript.%(ext)s" URL
# Get video metadata for context
yt-dlp --skip-download --print-json URL > "$ANALYSIS_DIR/metadata.json"
Handle long transcripts - For transcripts exceeding 8,000 tokens (roughly 6,000 words or 45+ minutes):
- Split into logical chunks based on timestamp or topic breaks
- Generate a summary for each chunk focusing on key concepts
- Create an overall synthesis connecting themes to the user's question
- Reference specific timestamps for detailed sections
For shorter transcripts, analyze directly without chunking.
Provide analysis in this format:
Video Overview:
- Title, author, duration
- Main topic and key themes
Key Insights:
- Concept 1 with timestamp
- Concept 2 with timestamp
- Technical approaches explained
Relevance to Your Project:
- Direct applications
- Differences from current approach
- Potential improvements or learnings
Specific Recommendations:
- Actionable items based on video content
- Code patterns or techniques to consider
Multiple languages: Prefer English transcripts using --sub-lang en. If unavailable, check available languages with --list-subs.
Long processing time: Set expectations for videos over 2 hours. Offer to focus on specific sections if timestamps provided.
Extract concrete examples and code patterns when available. Reference specific timestamps so users can jump to relevant sections.
When comparing with project code, be specific about similarities and differences. Vague comparisons like "similar approach" don't add value.
For technical content, identify the underlying patterns and principles rather than surface-level implementation details. Help users understand transferable concepts.
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.
- 25d ago First seen · 134 lines · 28 tokens per session scan A 8431224bae8f
youtube-transcript-analyzer is a skill published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 25d ago), licensed MIT. It adds 28 tokens to every session and 969 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-22.
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