Labsco
vercel-labs logo

ai-cli

★ 627

by vercel · part of vercel-labs/ai-cli

Generate text, images, and video from the terminal using AI models.

🔥🔥🔥✓ VerifiedAccount requiredNeeds API keys
#automation#ai-image#git
🧰 Not standalone. This skill ships with vercel-labs/ai-cli and only works together with that tool — install the tool first, then add this skill.

Generate text, images, and video from the terminal using AI models.

Inspect the full instructions your agent will receiveExpand

This is the exact playbook injected into your agent when the skill activates — shown here so you can audit it before installing. You don't need to read it to use the skill.

by vercel

Generate text, images, and video from the terminal using AI models. npx skills add https://github.com/vercel-labs/ai-cli --skill ai-cli Download ZIPGitHub627

ai-cli

Generate text, images, video, and audio from the terminal using AI models.

When to Use

Use when you need to:

  • Generate images from text prompts or existing images

  • Generate video from text prompts or images

  • Generate text (summaries, explanations, code reviews) from prompts or piped content

  • Generate speech from text or transcribe audio files and streams

  • Compare outputs across multiple models side-by-side

  • Build composable media pipelines by chaining commands via stdin/stdout

Commands

Copy & paste — that's it
ai text "explain this code" # generate text
ai image "a sunset over mountains" # generate an image
ai video "a spinning triangle" # generate a video
ai audio speak "hello" # generate speech
ai audio transcribe recording.mp3 # transcribe audio
ai models --type audio # list speech and transcription models

Key Flags

Copy & paste — that's it
-m, --model Model ID (provider/name or short name), comma-separated for multi-model
-o, --output Output file or directory
-n, --count Number of generations per model
-q, --quiet Suppress progress output
--json Output structured metadata as JSON (paths, timing, success/failure)

Piping Patterns

Chain commands for agent workflows:

Copy & paste — that's it
# Pipe content in for summarization
cat file.txt | ai text "summarize this"
git diff | ai text "write a commit message"

# Image-to-video pipeline
ai image "a dragon" | ai video "animate this"

# Image editing via stdin
cat photo.png | ai image "make it a watercolor"

# Audio workflows
echo "Ship the changelog" | ai audio speak -o changelog.mp3
cat recording.mp3 | ai audio transcribe -o transcript.txt

Structured Output

Use --json to get machine-readable results:

Copy & paste — that's it
ai image "a sunset" --json

Returns:

Copy & paste — that's it
{
 "elapsed_ms": 3420,
 "count": 1,
 "results": [
 {
 "index": 1,
 "model": "openai/gpt-image-2",
 "elapsed_ms": 3420,
 "success": true,
 "file": "/path/to/resp_abc123.png"
 }
 ]
}

Multi-Model Comparison

Copy & paste — that's it
ai image "a sunset" -m "openai/gpt-image-1,bfl/flux-2-pro,xai/grok-imagine-image"

Output Behavior

  • Interactive (TTY): saves to file, prints path to stderr

  • Piped (non-TTY): writes raw content to stdout for chaining

  • -o <dir>: saves inside directory with auto-generated names

When the CLI chooses a filename, it uses a response ID when available and falls back to a random 8-character ID, such as resp_abc123.png or 7f3a9c1d.mp3.

Important for agents: Always use -o to save to a file when generating images, video, or speech audio. Without -o in a non-TTY context, raw binary data is written to stdout, which wastes context and is not useful for agents. Use -o output.png, -o speech.mp3, or an output directory and read the file path from --json output instead.

Timeouts

  • text: 120 seconds

  • image: 300 seconds

  • video: 300 seconds

  • audio speak: 120 seconds

  • audio transcribe: 120 seconds

Exit Codes

  • 0 — success

  • 1 — all generations failed

  • 2 — partial failure (some succeeded)