MCP servers · Python · v0.1.0
Curated skills, sub-agents, and config templates that supercharge Claude Code — research, image gen, GitHub automation & more.
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Extract subtitles/transcripts from YouTube videos. Triggers: "youtube transcript", "extract subtitles", "video captions", "视频字幕", "字幕提取", "YouTube转文字", "提取字幕".
by feiskyer · updated 1mo ago · imported from GitHub
Extract subtitles/transcripts from a YouTube video URL and save them as a local file.
Input YouTube URL: $ARGUMENTS
Confirm the input is a valid YouTube URL (supports youtube.com/watch?v=, youtu.be/, and youtube.com/shorts/ formats). If no URL is provided via arguments, check the conversation context for a YouTube link.
Use command-line tools to quickly extract subtitles.
Execute which yt-dlp.
yt-dlp is found, proceed to 2.2.yt-dlp is not found, skip to Step 3.yt-dlp --cookies-from-browser=chrome --get-title "[VIDEO_URL]"
--cookies-from-browser to avoid sign-in restrictions. Default to chrome.firefox, safari, edge) and retry.yt-dlp --cookies-from-browser=chrome --write-auto-sub --write-sub --sub-lang zh-Hans,zh-Hant,en --skip-download --output "<Video Title>.%(ext)s" "[VIDEO_URL]"
yt-dlp saves subtitles as .vtt or .srt files. Convert the downloaded file to plain Timestamp Text format:
.vtt or .srt).<Video Title>.txt with one Timestamp Text entry per line.When the CLI method fails or yt-dlp is missing, use Chrome DevTools MCP to extract subtitles via browser UI automation.
Check if Chrome DevTools MCP tools are available (look for tools matching chrome__new_page or similar).
If Chrome DevTools MCP is not available and yt-dlp was not found in Step 2, stop and notify the user: "Unable to proceed. Please either install yt-dlp (for fast CLI extraction) or configure Chrome DevTools MCP (for browser automation)."
Use Chrome DevTools MCP new_page to open the video URL.
Use Chrome DevTools MCP take_snapshot to read the page accessibility tree.
The "Show transcript" button is usually hidden within the collapsed description area.
click to click that button.take_snapshot to get the updated UI.click to click that button.Directly reading the accessibility tree for long transcript lists is slow and token-heavy. Use Chrome DevTools MCP evaluate_script to run this JavaScript instead:
() => {
const segments = document.querySelectorAll("ytd-transcript-segment-renderer");
if (!segments.length) return "BUFFERING";
return Array.from(segments)
.map((seg) => {
const time = seg.querySelector(".segment-timestamp")?.innerText.trim();
const text = seg.querySelector(".segment-text")?.innerText.trim();
return `${time} ${text}`;
})
.join("\n");
};
If it returns "BUFFERING", wait a few seconds and retry (up to 3 attempts).
<Video Title>.txt.close_page to release resources.<Video Title>.txtTimestamp Subtitle Text.No comments yet — start the thread.
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MCP servers · Python · v0.1.0
Curated skills, sub-agents, and config templates that supercharge Claude Code — research, image gen, GitHub automation & more.
#agentic-ai#agents#ai
Prompts · Python · v0.1.0
Explore user intent, requirements, and design options through collaborative dialogue before implementation. Use before building new features, components, or systems — whenever the user describes something to build and design decisions are involved. Triggers: "brainstorm", "help me design", "think through the requirements", "头脑风暴", "设计方案", "梳理需求". Not for bug fixes, config changes, or tasks with an obvious implementation path.
#agentic-ai#agents#ai
Prompts · Python · v0.1.0
Leverage OpenAI Codex/GPT models for autonomous code implementation, code review, and plan review. Triggers: "codex", "use gpt", "gpt-5", "let openai", "full-auto", "adversarial review", "second opinion review", "用codex", "让gpt实现", "对抗式审查", "让codex审查计划", "第二意见". Use this skill whenever the user wants to delegate coding tasks to OpenAI models, run code or plan reviews via codex, get a second-opinion review from a different model, or execute tasks in a sandboxed environment.
#agentic-ai#agents#ai
Prompts · Python · v0.1.0
Multi-agent research orchestration: split a research goal into parallel sub-goals, run each via headless `claude -p` subprocesses, aggregate results into a polished report file. Use for systematic web/document research, competitive or industry analysis, batch link/dataset processing, and long-form evidence synthesis. Triggers: "深度调研", "deep research", "wide research", "多 Agent 调研", "系统调研".
#agentic-ai#agents#ai