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/video-transcribe

Video/audio transcription, visual frame analysis, Groq Whisper long-form transcripts, timestamped Obsidian notes, and keyframe-based visual summaries. Use for video links, audio links, 字幕/转录/视频总结/画面分析/图文笔记, especially when the result must replace watching the video. Keywords:

From plugin
all-my-ai-needs
1223 skills9 MCP
Install
$ npx -y skills add codingSamss/all-my-ai-needs --skill video-transcribe --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/video-transcribe

Context preview

The summary Claude sees to decide when to auto-load this skill.

Video/audio transcription, visual frame analysis, Groq Whisper long-form transcripts, timestamped Obsidian notes, and keyframe-based visual summaries. Use for video links, audio links, 字幕/转录/视频总结/画面分析/图文笔记, especially when the result must replace watching the video. Keywords:

SKILL.md

video-transcribe.SKILL.md
name: video-transcribe
description: "Video/audio transcription, visual frame analysis, Groq Whisper long-form transcripts, timestamped Obsidian notes, and keyframe-based visual summaries. Use for video links, audio links, 字幕/转录/视频总结/画面分析/图文笔记, especially when the result must replace watching the video. Keywords: video, transcribe, 转录, 视频, 音频, audio, subtitle, 字幕, summary, 总结, 图文笔记, 视频内容, 画面分析, visual analysis, keyframe, whisper, groq, yt-dlp"

Video Transcribe

Use this skill when the user asks to understand, transcribe, summarize, or visually analyze a video/audio source.

Mode Selection

  • **Full note / no omission**: user says 全量、完整、不要看视频、替代看视频、图文笔记, or asks about completeness. Download the source, transcribe the whole audio, extract frames, and produce a coverage-checked note.
  • **Audio transcript**: user explicitly asks for 字幕、转录、他说了什么、transcribe. Produce a timestamped transcript or transcript-backed summary.
  • **Visual analysis**: user explicitly asks about 画面、截图、视觉、展示了什么. Extract frames and analyze visuals; skip Groq unless speech content matters.
  • **Quick summary**: user only wants a brief answer. Summarize, but still state whether the result is transcript-backed, frame-backed, or both.

When intent is unclear, default to transcript + keyframes for short videos and ask before spending API quota on long videos.

Core Rules

  • For long-form technical content, prefer Groq `whisper-large-v3` with `response_format=verbose_json`; do not use plain `text` when timestamps or completeness checks matter.
  • Do not force `language=zh` for non-Chinese videos. Use the detected language or set `--language en` for English technical videos.
  • Use a terminology prompt for product/tool names such as `Codex`, `Remotion`, `Supabase`, `Typefully`, `TestFlight`, `Vercel`, `Claude Code`.
  • Source timestamps are an outline, not proof. Verify every listed timestamp has transcript coverage before claiming the note can replace watching the video.
  • Do not add a personal status tag like `已读`; that belongs to the user, not the agent.
  • Keep full transcripts in the working directory unless the user asks to store them in the note. The published note should contain structured coverage, excerpts/paraphrase, screenshots, and validation notes.

Standard Workflow

1. Create a work directory under `/tmp/video-transcribe/<slug>`. 2. Download media with `scripts/download_media.sh`. 3. Transcribe with `scripts/transcribe_groq.py` when speech content is needed. 4. Extract frames with `scripts/extract_frames.sh` when visual context or screenshots are needed. 5. For Obsidian notes, read `references/obsidian-video-note.md` before writing. 6. Validate deliverables with `scripts/verify_obsidian_note.sh` and run `touch <note>` after editing an Obsidian file externally.

Example:

WORK=/tmp/video-transcribe/codex-super-app
mkdir -p "$WORK"

SKILL_DIR="$HOME/.codex/skills/video-transcribe"
# In this repo, use: SKILL_DIR=platforms/codex/skills/video-transcribe
# In Claude runtime, use: SKILL_DIR="$HOME/.claude/skills/video-transcribe"

VIDEO=$("$SKILL_DIR/scripts/download_media.sh" "$URL" "$WORK" full)

"$SKILL_DIR/scripts/transcribe_groq.py" \
  "$VIDEO" \
  --work-dir "$WORK" \
  --language en \
  --prompt "Technical terms: Codex, Remotion, Supabase, Typefully, TestFlight, Vercel, Claude Code."

"$SKILL_DIR/scripts/extract_frames.sh" \
  "$VIDEO" "$WORK/frames" --count 16

Output Standards

For a note intended to replace watching a video, use two layers:

  • **Readable layer**: a short executive summary, key claims, reusable playbook, decisions, tools, gotchas, and screenshots.
  • **Coverage layer**: collapsible timestamp groups or phase tables that map every source timestamp to the transcript-backed note.

Avoid a single flat list of dozens of timestamps. It is technically complete but hard to read.

Bundled Resources

  • `scripts/download_media.sh`: yt-dlp wrapper with cookie retry and `uvx --from yt-dlp` fallback.
  • `scripts/transcribe_groq.py`: media-to-audio extraction, size-based segmentation, Groq transcription, and timestamp merge. Writes `transcript.txt` (one segment per line), `transcript_segments.json`, and `transcript_global.md`.
  • `scripts/extract_frames.sh`: uniform or timestamp-based keyframe extraction.
  • `scripts/verify_obsidian_note.sh`: Markdown image/timestamp/frontmatter checks.
  • `references/obsidian-video-note.md`: long-form Obsidian note structure and coverage standard.
  • `references/troubleshooting.md`: common yt-dlp, Groq, ffmpeg, and note-validation failures.

Load reference files only when the current request needs that detail.

Read more
Ships withall-my-ai-needs

跨 Claude Code 与 Codex 两套 agent 的 skill 能力真源与同步规则。写入由 agent 执行,人只审校与决策。 把 Claude Code 与 Codex 的可复用能力收敛到一个仓库,按 platform-first 维护:每个平台独立持有自己的 skills/ 与运行约定,同名 skill 允许在两端并存,不强行抽象去重。 仓库要解决的是多端 AI 配置的漂移——GitHub 仓库、本地工作区、本地 CLI

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