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Video Transcripts

OrganizationPopular
udecode
video-transcripts

Transcribe a supplied local or linked video with Gemini Files API when its contents are needed as evidence.

Overview

Publisherudecode
Repositorydotai
Skill namevideo-transcripts
Stars
1.2K
Forks
82
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by udecode on GitHub. Read the source before you install it.

Installation

Install the Video Transcripts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/udecode/dotai/tree/main/skills/video-transcripts
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/udecode/dotai.git /tmp/dotai
mkdir -p .claude/skills
cp -r /tmp/dotai/skills/video-transcripts .claude/skills/video-transcripts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Video Transcripts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Video Transcripts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Video Transcripts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Video Transcripts

Quick Start

Run the helper once per relevant video. <skill> is this skill's directory:

bash
bash <skill>/scripts/generate_video_transcript.sh \
  "https://uploads.linear.app/.../screen-recording.mov" \
  --title "PDF preview hyperlinks trigger leave-page modal"

Or for a GitHub attachment:

bash
bash <skill>/scripts/generate_video_transcript.sh \
  "https://github.com/user-attachments/assets/..." \
  --title "Slash menu loses selection after confirm"

Or for a Screencastify watch page:

bash
bash <skill>/scripts/generate_video_transcript.sh \
  "https://app.screencastify.com/watch/..." \
  --title "Bulk send expands filtered recipients"

Or for a local file:

bash
bash <skill>/scripts/generate_video_transcript.sh \
  "/absolute/path/to/video.mov" \
  --title "Preview hyperlink exits workflow"

For auth-gated Linear uploads, the helper automatically retries with cookies from the local Linear desktop app on macOS. For private GitHub asset URLs, it retries with GITHUB_TOKEN, GH_TOKEN, or gh auth token when available.

Use This When

  • A GitHub or Linear issue, PR, or comment includes a screen recording.
  • An attachment URL points to uploads.linear.app.
  • An attachment URL points to a GitHub attachment or private GitHub asset host.
  • A tracker links to a public Screencastify watch page.
  • You need timeline-style transcript lines, not a vague summary.
  • You want the result in this exact XML shape:
xml
<video-transcripts>
<video-transcript title="...">
[00:00] (...)
</video-transcript>
</video-transcripts>

Workflow

Tracker comments and replies require explicit message authority. Prepare or return the transcript locally when publication is not authorized. Cache location and reuse rules below do not grant permission to post.

  1. Run the helper once for each relevant video.
  2. Give each run a short, bug-focused --title.
  3. For tracked work, the canonical shared cache should live in the tracker next to the evidence it describes.
  4. If the video is in the issue or PR body, use a top-level tracker comment.
  5. If the video is in a Linear comment, post the transcript cache as a reply to that specific comment.
  6. If the video is in a GitHub issue or PR comment, use one dedicated top-level cache comment for that source comment's video set. GitHub has no replies, so keep cache comments separated by source container instead of merging unrelated comment videos together.
  7. Cache comment or reply body should start with a transcript source link, then the timestamp lines:
md
[[Transcript](link to video or comment if not available)]
[00:00] (...)
  1. When caching helper XML, strip the <video-transcripts> and <video-transcript ...> wrapper lines and paste only timestamp lines after the source link.
  2. If there are multiple videos in the same source container, repeat the [[Transcript](...)] source link plus timestamp lines for each video.
  3. Do not hand-write or paraphrase video behavior when the helper can run. Use the actual transcript output.
  4. Link [[Transcript](...)] to the video URL when available. If the video URL is not available or is unstable, link to the source comment that contains the video.
  5. For signed tracker-hosted URLs like uploads.linear.app, strip the query string so a new signature does not invalidate an otherwise valid cache entry. For Screencastify, cache against the stable https://app.screencastify.com/watch/<id> URL, not the signed dash-assets HLS URLs.
  6. Do not add decorative metadata like title to cached transcript entries unless a later workflow truly needs it.
  7. Before re-transcribing for tracked work, match cache entries by source container first:
    • one cache comment for issue or PR body videos
    • one Linear reply per comment containing video(s)
    • one dedicated GitHub cache comment per issue or PR comment containing video(s)
  8. If the matching cache already covers the current normalized video keys for that source container, reuse it. Only transcribe missing or new video evidence.
  9. Do not use docs/ for raw tracker transcript cache. That is durable repo knowledge space, not raw issue evidence.
  10. If you invoke this through codex exec, prefer -o <file> so the final XML is captured without CLI progress chatter.

Output Contract

  • Return XML, not Markdown.
  • Use one [MM:SS] (...) line per observed action or system response.
  • Quote visible UI text when legible.
  • Describe only visible actions, screen changes, and audible speech if present.
  • Do not invent hidden state, motives, or implementation details.
  • Prefer concise, high-signal lines over per-keystroke sludge.

Model Strategy

The helper defaults to gemini-3.1-flash-lite-preview for cost efficiency.

If that output is malformed, too thin, or obviously noisy, it retries with gemini-3-flash-preview.

For Gemini 3 models, the helper forces minimal thinking so output budget goes to the transcript instead of hidden reasoning.

Override with:

bash
VIDEO_TRANSCRIPTS_MODEL=gemini-3-flash-preview bash <skill>/scripts/generate_video_transcript.sh ...

Or:

bash
bash <skill>/scripts/generate_video_transcript.sh ... \
  --model gemini-2.5-flash

Notes

  • The helper accepts a local file path or remote URL.
  • For uploads.linear.app URLs, it first tries LINEAR_COOKIE_HEADER, then LINEAR_COOKIES_DB, then falls back to the local Linear desktop cookie store at ~/Library/Application Support/Linear/Cookies.
  • For GitHub asset URLs, it first tries GITHUB_TOKEN, then GH_TOKEN, then gh auth token, then falls back to an unauthenticated download.
  • For Screencastify watch URLs, it calls https://umbrella.svc.screencastify.com/api/umbrellaService/watch/<id>, resolves the signed HLS manifest, rewrites child playlists so segments keep the signature, and remuxes the recording with ffmpeg.
  • Screencastify URL support requires ffmpeg.
  • It looks for GEMINI_API_KEY, then GOOGLE_API_KEY.
  • If neither is set, it tries ~/.bash_profile before failing.
  • Use --debug-dir <dir> when you want request and response artifacts saved.
  • Quality gate rejects obvious low-signal transcripts such as repeated partial typing runs and falls back automatically.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Video Transcripts AI skill do?

Transcribe a supplied local or linked video with Gemini Files API when its contents are needed as evidence.

Why use Video Transcripts on TypingMind?

Because you install it once and use it with any model. Video Transcripts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Video Transcripts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/udecode/dotai/tree/main/skills/video-transcripts. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Video Transcripts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Video Transcripts?

As many as you like. As long as a model supports skills, you can use Video Transcripts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Video Transcripts AI skill free?

It is published on GitHub by udecode. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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