gh-rawveg-skillsforge-marketplace-replicate-cli/skills/replicate-cli/SKILL.md

name, description

name

description

replicate-cli

This skill provides comprehensive guidance for using the Replicate CLI to run AI models, create predictions, manage deployments, and fine-tune models. Use this skill when the user wants to interact with Replicate's AI model platform via command line, including running image generation models, language models, or any ML model hosted on Replicate. This skill should be used when users ask about running models on Replicate, creating predictions, managing deployments, fine-tuning models, or working with the Replicate API through the CLI.

Replicate CLI

The Replicate CLI is a command-line tool for interacting with Replicate's AI model platform. It enables running predictions, managing models, creating deployments, and fine-tuning models directly from the terminal.

Authentication

Before using the Replicate CLI, set the API token:

export REPLICATE_API_TOKEN=<token-from-replicate.com/account>

Alternatively, authenticate interactively:

replicate auth login

Verify authentication:

replicate account current

Core Commands

Running Predictions

The primary use case is running predictions against hosted models.

Basic prediction:

replicate run <owner/model> input_key=value

Examples:

Image generation:

replicate run stability-ai/sdxl prompt="a studio photo of a rainbow colored corgi"

Text generation with streaming:

replicate run meta/llama-2-70b-chat --stream prompt="Tell me a joke"

Prediction flags:

• --stream - Stream output tokens in real-time (for text models)
• --no-wait - Submit prediction without waiting for completion
• --web - Open prediction in browser
• --json - Output result as JSON
• --save - Save outputs to local directory
• --output-directory <dir> - Specify output directory (default: ./{prediction-id})

Input Handling

File uploads: Prefix local file paths with @:

replicate run nightmareai/real-esrgan image=@photo.jpg

Output chaining: Use {{.output}} template syntax to chain predictions:

replicate run stability-ai/sdxl prompt="a corgi" | \
replicate run nightmareai/real-esrgan image={{.output[0]}}

Model Operations

View model schema (see required inputs and outputs):

replicate model schema <owner/model>
replicate model schema stability-ai/sdxl --json

List models:

replicate model list
replicate model list --json

Show model details:

replicate model show <owner/model>

Create a new model:

replicate model create <owner/name> \
  --hardware gpu-a100-large \
  --private \
  --description "Model description"

Model creation flags:

• --hardware <sku> - Hardware SKU (see references/hardware.md)
• --private / --public - Visibility setting
• --description <text> - Model description
• --github-url <url> - Link to source repository
• --license-url <url> - License information
• --cover-image-url <url> - Cover image for model page

Training (Fine-tuning)

Fine-tune models using the training command:

replicate train <base-model> \
  --destination <owner/new-model> \
  input_key=value

Example - Fine-tune SDXL with DreamBooth:

replicate train stability-ai/sdxl \
  --destination myuser/custom-sdxl \
  --web \
  input_images=@training-images.zip \
  use_face_detection_instead=true

List trainings:

replicate training list

Show training details:

replicate training show <training-id>

Deployments

Deployments provide dedicated, always-on inference endpoints with predictable performance.

Create deployment:

replicate deployments create <name> \
  --model <owner/model> \
  --hardware <sku> \
  --min-instances 1 \
  --max-instances 3

Example:

replicate deployments create text-to-image \
  --model stability-ai/sdxl \
  --hardware gpu-a100-large \
  --min-instances 1 \
  --max-instances 5

Update deployment:

replicate deployments update <name> \
  --max-instances 10 \
  --version <version-id>

List deployments:

replicate deployments list

Show deployment details and schema:

replicate deployments show <name>
replicate deployments schema <name>

Hardware

List available hardware options:

replicate hardware list

See references/hardware.md for detailed hardware information and selection guidelines.

Scaffolding

Create a local development environment from an existing prediction:

replicate scaffold <prediction-id-or-url> --template=<node|python>

This generates a project with the prediction's model and inputs pre-configured.

Command Aliases

For convenience, these aliases are available:

Alias	Equivalent Command
replicate run	replicate prediction create
replicate stream	replicate prediction create --stream
replicate train	replicate training create

Short aliases for subcommands:

• replicate m = replicate model
• replicate p = replicate prediction
• replicate t = replicate training
• replicate d = replicate deployments
• replicate hw = replicate hardware
• replicate a = replicate account

Common Workflows

Image Generation Pipeline

Generate an image and upscale it:

replicate run stability-ai/sdxl \
  prompt="professional photo of a sunset" \
  negative_prompt="blurry, low quality" | \
replicate run nightmareai/real-esrgan \
  image={{.output[0]}} \
  --save

Check Model Inputs Before Running

Always check the model schema to understand required inputs:

replicate model schema owner/model-name

Batch Processing

Run predictions and save outputs:

for prompt in "cat" "dog" "bird"; do
  replicate run stability-ai/sdxl prompt="$prompt" --save --output-directory "./outputs/$prompt"
done

Monitor Long-Running Tasks

Submit without waiting, then check status:

# Submit
replicate run owner/model input=value --no-wait --json > prediction.json

# Check status later
replicate prediction show $(jq -r '.id' prediction.json)

Best Practices

1. Always check schema first - Run replicate model schema <model> to understand required and optional inputs before running predictions.

2. Use streaming for text models - Add --stream flag when running language models to see output in real-time.

3. Save outputs explicitly - Use --save and --output-directory to organize prediction outputs.

4. Use JSON output for automation - Add --json flag when parsing outputs programmatically.

5. Open in web for debugging - Add --web flag to view predictions in the Replicate dashboard for detailed logs.

6. Chain predictions efficiently - Use the {{.output}} syntax to pass outputs between models without intermediate saves.

Troubleshooting

Authentication errors:

• Verify REPLICATE_API_TOKEN is set correctly
• Run replicate account current to test authentication

Model not found:

• Check model name format: owner/model-name
• Verify model exists at replicate.com

Input validation errors:

• Run replicate model schema <model> to see required inputs
• Check input types (string, number, file)

File upload issues:

• Ensure @ prefix is used for local files
• Verify file path is correct and file exists

Additional Resources

• Replicate documentation: https://replicate.com/docs
• Model explorer: https://replicate.com/explore
• API reference: https://replicate.com/docs/reference/http
• GitHub repository: https://github.com/replicate/cli