Build integrated IS/BS/CF financial workbooks in Excel.
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YouTube transcripts to summaries, threads, blogs.
中文简介正在准备中。当前显示原文说明。
安装前可以查看交给 AI 代理的指令内容。
Use when the user shares a YouTube URL or video link, asks to summarize a video, requests a transcript, or wants to extract and reformat content from any YouTube video. Transforms transcripts into structured content (chapters, summaries, threads, blog posts).
Extract transcripts from YouTube videos and convert them into useful formats.
Use uv so the dependency is installed into the same OpenAmer-managed environment
that runs the helper script:
uv pip install youtube-transcript-api
SKILL_DIR is the directory containing this SKILL.md file. The script accepts any standard YouTube URL format, short links (youtu.be), shorts, embeds, live links, or a raw 11-character video ID.
# JSON output with metadata
uv run python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID"
# Plain text (good for piping into further processing)
uv run python3 SKILL_DIR/scripts/fetch_transcript.py "URL" --text-only
# With timestamps
uv run python3 SKILL_DIR/scripts/fetch_transcript.py "URL" --timestamps
# Specific language with fallback chain
uv run python3 SKILL_DIR/scripts/fetch_transcript.py "URL" --language tr,en
After fetching the transcript, format it based on what the user asks for:
00:00 Introduction — host opens with the problem statement
03:45 Background — prior work and why existing solutions fall short
12:20 Core method — walkthrough of the proposed approach
24:10 Results — benchmark comparisons and key takeaways
31:55 Q&A — audience questions on scalability and next steps
--text-only --timestamps via uv run python3.--language to get any available transcript. If still empty, tell the user the video likely has transcripts disabled.--language to fetch any available transcript, then note the actual language to the user.uv pip install youtube-transcript-api and retry.还没有评价。欢迎分享你的使用感受。
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Build integrated IS/BS/CF financial workbooks in Excel.
中文简介正在准备中。当前显示原文说明。
Run and grow the OpenAmer Agent-to-Agent swarm: identity, trust, node-to-node ask, signed skill/insight sharing, and the autonomous self-learning loop.
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Use for A/B experiments on OpenAmer configs and skills.
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Roleplay a hostile user to find and triage UX pain points.
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Agent mesh: master/worker nodes, HTTP delegation, heartbeat.
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Use for agentic commerce payments tasks. Candidate synthesized from trend radar; awaiting trial promotion.
中文简介正在准备中。当前显示原文说明。