Technical & DevelopmentIntermediate
replicate
Discover, compare, and run AI models using Replicate's API
Developer Setup
Setup & Installation
bash
npx skills add https://github.com/replicate/skills --skill replicatenpx skills add https://github.com/replicate/skills --skill replicateOr paste this URL into your assistant to install:
Overview
What This Skill Does
Replicate lets you run AI models via API, including image generation, language models, and other ML tasks. It handles model hosting, scaling, and versioning so you don't run your own GPU infrastructure. Models are searchable and runnable through a standardized REST interface.
Application
When to use this Skill
- Configuring integration settings for custom agent workflows.
- Optimizing query execution and response latency in production.
- Developing clean, standard-compliant implementations for enterprise services.
- Troubleshooting connection timeouts and authentication handshakes.
- Monitoring API rate limits and execution pipelines programmatically.
Documentation
Show Skills.md file
Docs
- Reference: https://replicate.com/docs/llms.txt
- OpenAPI schema: https://api.replicate.com/openapi.json
- MCP server: https://mcp.replicate.com
- Per-model docs:
https://replicate.com/{owner}/{model}/llms.txt - Set
Accept: text/markdownwhen requesting docs pages for Markdown responses.
Workflow
- Choose the right model - Search with the API or ask the user.
- Get model metadata - Fetch input and output schema via API.
- Create prediction - POST to /v1/predictions.
- Poll for results - GET prediction until status is "succeeded".
- Return output - Usually URLs to generated content.
Three ways to get output
- Create a prediction, store its id from the response, and poll until completion.
- Set a
Prefer: waitheader when creating a prediction for a blocking synchronous response. Only recommended for very fast models. Max 60 seconds. - Set an HTTPS webhook URL when creating a prediction, and Replicate will POST to that URL when the prediction completes.
Guidelines
- Use the
POST /v1/predictionsendpoint, as it supports both official and community models.
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