AugmentClaude
VideoSocial MediaResearch

Media Transcriber

By tonhowtf· tonhowtf/omniget· 0

Transcribe and summarize video, audio, and social media content from links or files.

Installation

  1. Make sure Claude is on your device and in your terminal.

    Skills load from ~/.claude/skills/ when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then run claude in any terminal to verify.

    One-time setup
    npm i -g @anthropic-ai/claude-code

    Already have it? Skip ahead.

  2. Paste into Claude Code or into your terminal.
    Install
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    This copies the whole skill folder into ~/.claude/skills/omniget-transcribe-tonhowtf/ — the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.

    Faster alternative (instruction-only skills)

    Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.

    Quick install (SKILL.md only)
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  3. Restart Claude Code.

    Quit and reopen Claude Code (or any other agent that loads from ~/.claude/skills/). New skills are picked up on startup.

  4. Just ask Claude.

    Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.

Prefer to read the source first? Open on GitHub.

When Claude uses it

This skill should be used when the user shares a video, audio, podcast or social-post URL or a local media file and asks "what does this video say", "transcribe this", "summarize this video", "get the transcript", "what is this reel about", "research this post", "pull the caption", "turn this talk into notes", "quote the part where", "translate what they say", or wants to analyze spoken content from a link. Covers YouTube, TikTok, Instagram, X/Twitter, Reddit, Vimeo, Twitch, Bilibili, Threads and any local .mp4/.mp3/.wav/.m4a.

What this skill does

Transcribing and researching media

Turn a link or file into text with ${CLAUDE_PLUGIN_ROOT}/scripts/, then read the transcript and answer. Claude cannot hear audio, so a speech-to-text step always runs first. Each script prints one JSON line on success; progress goes to stderr.

Pipeline

  1. Describe the source (URL only, no download): 
    bash "${CLAUDE_PLUGIN_ROOT}/scripts/research.sh" "<url>"
    Returns title, platform, uploader, duration, has_captions, and caption (the post text or description) when the platform provides one. Skip this step for local files.
  2. Get the transcript: 
    bash "${CLAUDE_PLUGIN_ROOT}/scripts/transcribe.sh" "<url-or-file>" [--backend auto|captions|local|mlx|gemini|openai] [--model NAME] [--lang xx] [--out DIR]
    Returns transcript (Markdown with [mm:ss] markers), srt when timestamps exist, backend, model, duration, chars. Files land in ~/Downloads/omniget/transcripts unless --out or OMNIGET_DIR says otherwise.
  3. Read the .md file and do what was asked: summarize, extract key points, quote with timestamps, translate, or write a research note that combines the caption and the transcript. Cite moments as [mm:ss]. Do not paste the whole transcript back unless asked.

Backend ladder (--backend auto)

OrderBackendNeedsCostNotes
1captionsplatform subtitlesfree, secondstried first for every URL; auto-captions are rough but usable
2localwhisper-cli + a ggml modelfree, privateCPU/Metal; large-v3-turbo-q5_0 is the default model choice
3mlxmlx_whisper (Apple Silicon)free, privatefaster than whisper-cli on M-series Macs
4geminiGEMINI_API_KEYcents per houraudio-native; good for long recordings
5openaiOPENAI_API_KEYabout $0.006 per minutewhisper-1 returns timestamps; --model gpt-4o-transcribe is more accurate but text-only

Choose deliberately when the user states a preference:

  • "most accurate" with an OpenAI key: --backend openai --model gpt-4o-transcribe.
  • "free" or "offline" or "private": --backend local (or mlx on a Mac).
  • Known language: pass --lang en (or pt, es, ...) to every backend; it speeds up and sharpens results.
  • Recording longer than two hours: say so and confirm before a cloud backend runs.

Keys are read from the environment or ~/.config/ai-keys.env. Never print them. To add one, the user runs bash "${CLAUDE_PLUGIN_ROOT}/scripts/keys.sh" set in their own terminal (hidden input); never ask them to paste a key here. keys.sh check (or doctor.sh --check-keys) reports which keys work.

Exit codes and what to do

ExitMeaningAction
3--backend captions and the platform has nonererun with --backend auto
4no local engine and no keyoffer setup.sh (installs tools, confirm once) and tell the user to add a key with keys.sh set in their own terminal; get-model.sh large-v3-turbo-q5_0 fetches a local model (547 MB)
1tool missing or download failedread the -> hint the script printed (rate-limit / login / DRM); run doctor.sh, or setup.sh --update if a site broke; for login errors see the omniget-fetch error table

Rules

  • Never start an install or a model download without the user's explicit yes.
  • Summaries state which backend produced the transcript when accuracy matters (auto-captions vs. Whisper).
  • Keep .srt and .md next to each other; the user may open the SRT in OmniGet's Subtitle Workshop.

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