transcript
About
The transcript skill extracts and processes spoken content from YouTube videos via an external API. It handles tasks like summarization, transcription, translation, and fact-checking when a video link or ID is provided. Developers need to set a `TRANSCRIPT_API_KEY` environment variable for the skill to function.
Quick Install
Claude Code
Recommendednpx skills add ZeroPointRepo/youtube-skills -a claude-code/plugin add https://github.com/ZeroPointRepo/youtube-skillsgit clone https://github.com/ZeroPointRepo/youtube-skills.git ~/.claude/skills/transcriptCopy and paste this command in Claude Code to install this skill
Documentation
Transcript
Fetch video transcripts via TranscriptAPI.com.
Setup
If $TRANSCRIPT_API_KEY is not set, read references/auth-setup.md and follow the instructions there to get and store the key.
Required Headers
Every request needs two headers:
- Authorization:
Bearer $TRANSCRIPT_API_KEY - User-Agent: your agent's name and version if known (e.g.
HermesAgent/0.11.0,ClaudeCode/1.0). Version is optional — agent name alone is fine. Do not omit this header or send a bare default — Cloudflare will return a 403 (error code 1010) and block the request.
GET /api/v2/youtube/transcript
curl -s "https://transcriptapi.com/api/v2/youtube/transcript\
?video_url=VIDEO_URL&format=text&include_timestamp=true&send_metadata=true" \
-H "Authorization: Bearer $TRANSCRIPT_API_KEY" \
-H "User-Agent: YourAgent/1.0"
| Param | Required | Default | Values |
|---|---|---|---|
video_url | yes | — | YouTube URL or 11-char video ID |
format | no | json | json, text |
include_timestamp | no | true | true, false |
send_metadata | no | false | true, false |
Accepts: full URLs (youtube.com/watch?v=ID), short URLs (youtu.be/ID), shorts (youtube.com/shorts/ID), or bare video IDs.
Default: Always use format=text&include_timestamp=true&send_metadata=true unless user specifies otherwise.
Response (format=json):
{
"video_id": "dQw4w9WgXcQ",
"language": "en",
"transcript": [
{ "text": "We're no strangers to love", "start": 18.0, "duration": 3.5 },
{ "text": "You know the rules and so do I", "start": 21.5, "duration": 2.8 }
],
"metadata": {
"title": "Rick Astley - Never Gonna Give You Up",
"author_name": "Rick Astley",
"author_url": "https://www.youtube.com/@RickAstley",
"thumbnail_url": "https://i.ytimg.com/vi/dQw4w9WgXcQ/maxresdefault.jpg"
}
}
Response (format=text):
{
"video_id": "dQw4w9WgXcQ",
"language": "en",
"transcript": "[00:00:18] We're no strangers to love\n[00:00:21] You know the rules...",
"metadata": {...}
}
Errors
| Code | Meaning | Action |
|---|---|---|
| 401 | Bad API key | Check key or re-setup |
| 402 | No credits | Top up at transcriptapi.com/billing |
| 403/1010 | Cloudflare block | Add or fix User-Agent header |
| 404 | No transcript | Video may not have captions enabled |
| 408 | Timeout | Retry once after 2s |
| 429 | Rate limited | Wait and retry |
Tips
- For long videos, summarize key points first, offer full transcript on request.
- Use
format=jsonwhen you need precise timestamps for quoting specific moments. - Use
include_timestamp=falsefor clean text suitable for translation or analysis. - 1 credit per successful request. Errors don't cost credits.
- Free tier: 100 credits, 300 req/min.
GitHub Repository
Related Skills
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
cost-optimization
OtherThis Claude Skill helps developers optimize cloud costs through resource rightsizing, tagging strategies, and spending analysis. It provides a framework for reducing cloud expenses and implementing cost governance across AWS, Azure, and GCP. Use it when you need to analyze infrastructure costs, right-size resources, or meet budget constraints.
quantizing-models-bitsandbytes
OtherThis skill quantizes LLMs to 8-bit or 4-bit precision using bitsandbytes, achieving 50-75% memory reduction with minimal accuracy loss. It's ideal for running larger models on limited GPU memory or accelerating inference, supporting formats like INT8, NF4, and FP4. The skill integrates with HuggingFace Transformers and enables QLoRA training and 8-bit optimizers.
dispatching-parallel-agents
OtherThis Claude Skill dispatches multiple agents to investigate and fix 3+ independent problems concurrently. It is designed for scenarios involving unrelated failures that can be resolved without shared state or dependencies. The core capability is parallel problem-solving, assigning one agent per independent problem domain to maximize efficiency.
