Videoink

videoink — video-to-article skill (skill-native, zero API cost)

When to invoke

The user supplies a public video URL AND asks for textual output derived from the video. Typical phrasings:

• "Summarize this video as a blog post"
• "Turn this podcast into a newsletter"
• "Write an article from this YouTube video"
• "把这个视频/B站链接写成一篇文章"
• "Give me a transcript of this video" (→ run only steps 1-2 below)

Do NOT invoke for

• Questions about the video that can be answered from metadata alone.
• Requests to download the video as a file (→ videoink fetch directly, or plain yt-dlp).
• Editing / polishing an article the user has already pasted in.
• Requests to publish the result somewhere (this skill stops at Markdown on disk).

Prerequisites

• videoink installed with local-whisper extra: pip install 'videoink[local]'.
• ffmpeg on PATH (used by yt-dlp).
• No LLM API key required in skill-native mode.

Default workflow (skill-native, 3 steps)

1. videoink fetch --mode audio                     # downloads audio only
2. videoink transcribe --engine local       # offline faster-whisper
3. YOU (the assistant) read the transcript.json and write article.md

Step 1 — fetch audio

videoink fetch --mode audio  --out-dir ./output//

The slug (`) comes from the video's canonical id; pick it from videoink probe --json if you want control, else let videoink full` derive it.

Step 2 — transcribe locally

videoink transcribe --engine local ./output//*.audio.m4a

Produces *.transcript.json (full segments + timestamps) and *.transcript.txt. Offline. No API key.

For a non-English source: add --language zh / --language ja / etc.

Step 3 — YOU write the article

Don't call videoink generate. Instead, do it yourself:

1. Read the style rules by opening videoink/styles/.md (from the installed package). default is the neutral blog / newsletter voice. Pick technical if the user asks for a dev-blog tone.
2. Read the transcript file (*.transcript.json or the .txt).
3. Write the article directly into ./output//article.md, following the style rules strictly.
4. Hand the user the absolute path to article.md.

Why you, not videoink generate: in Claude Code you ARE the LLM. Calling videoink generate would open a second HTTP billing channel (OpenAI/Anthropic) that the user has to fund separately. Writing it yourself is faster, cheaper, and uses the model the user already has.

Output contract

./output//
    article.md                   ← YOU wrote this
    *.transcript.json            ← faster-whisper output
    *.transcript.txt
    *.audio.m4a
    images/                      ← empty in v0.1

Hand the user the absolute path to article.md. Do not publish anywhere.

Mapping user intent to flags / style

| User signal | Action | |---|---| | "Developer-audience / technical tone" | read videoink/styles/technical.md | | "The speaker is in Mandarin / Japanese / ..." | videoink transcribe ... --language zh | | "Save into project X" | --out-dir ./projects/X/output/ | | "Use a bigger/smaller Whisper" | videoink transcribe ... --model small (or tiny/medium/large-v3) |

Default: local-engine base model, default style, output under ./output//.

Error recovery

faster-whisper not installed: the command prints pip install 'videoink[local]'. Surface it to the user verbatim.

yt-dlp format extraction fails: run videoink probe , inspect formats, re-run with an explicit --audio-format .

Standalone / non-skill usage (for reference)

When NOT running inside Claude Code (e.g. cron, CI, headless server), the skill-native flow doesn't apply — there's no LLM "you" to write the article. In those cases use:

• videoink full --engine local --provider openai --model gpt-4o — drives the generate step via an external LLM (needs OPENAI_API_KEY or ANTHROPIC_API_KEY).
• videoink generate — same, but standalone.

These still exist; they're just the wrong default inside Claude Code.

What this skill does NOT do (v0.1 scope)

• Does not upload, publish, or post the article.
• Does not fetch or embed images from the web (v0.2).
• Does not authenticate against Bilibili 1080P+ / member content (v0.2).
• Does not invent facts unsupported by the transcript; the built-in styles rule this out explicitly.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: zhujian0409
• Source: zhujian0409/videoink
• License: MIT

Install and usage instructions live in the source repository linked above.