mcpField
R
replicate

replicate

github.com/replicateDevelopment145459 installsUpdated 22w ago
Repository

About replicate

replicate is a skill for running models through the Replicate API. It is for users who need to find models, submit predictions, and retrieve results, with schema checks and polling, synchronous wait, or webhook-based completion handling.

Key features

  1. 01Search models and curated collections
  2. 02Fetch model metadata and schemas
  3. 03Create and poll predictions
  4. 04Use wait responses or webhooks
  5. 05Validate inputs and handle file outputs

Use cases

  1. 01Find a Replicate model for a task
  2. 02Prepare valid inputs from a model schema
  3. 03Run predictions asynchronously and collect results
  4. 04Receive completion notifications through webhooks

MCP support

The README provides the Replicate MCP server address but does not explain how this skill uses or connects to it.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/replicate/skills --skill replicate

After installing, type/replicatein Claude Code to confirm it loaded.

Details

From the project README

Docs

Workflow

Here's a common workflow for using Replicate's API to run a model:

  1. Choose the right model - Search with the API or ask the user
  2. Get model metadata - Fetch model input and output schema via API
  3. Create prediction - POST to /v1/predictions
  4. Poll for results - GET prediction until status is "succeeded"
  5. Return output - Usually URLs to generated content

Choosing models

  • Use the search and collections APIs to find and compare the best models. Do not list all the models via API, as it's basically a firehose.
  • Collections are curated by Replicate staff, so they're vetted.
  • Official models are in the "official" collection.
  • Use official models because they:
    • are always running
    • have stable API interfaces
    • have predictable output pricing
    • are maintained by Replicate staff
  • If you must use a community model, be aware that it can take a long time to boot.
  • You can create always-on deployments of community models, but you pay for model uptime.

Running models

Models take time to run. There are three ways to run a model via API and get its output:

  1. Create a prediction, store its id from the response, and poll until completion.
  2. Set a Prefer: wait header when creating a prediction for a blocking synchronous response. Only recommended for very fast models.
  3. Set an HTTPS webhook URL when creating a prediction, and Replicate will POST to that URL when the prediction completes.

Follow these guideliness when running models:

  • Use the "POST /v1/predictions" endpoint, as it supports both official and community models.
  • Every model has its own OpenAPI schema. Always fetch and check model schemas to make sure you're setting valid inputs. Even popular models change their schemas.
  • Validate input parameters against schema constraints (minimum, maximum, enum values). Don't generate values that violate them.
  • When unsure about a parameter value, use the model's default example or omit the optional parameter.
  • Don't set optional inputs unless you have a reason to. Stick to the required inputs and let the model's defaults do the work.
  • Use HTTPS URLs for file inputs whenever possible. You can also send base64-encoded files, but they should be avoided.
  • Fire off multiple predictions concurrently. Don't wait for one to finish before starting the next.
  • Output file URLs expire after 1 hour, so back them up if you need to keep them, using a service like Cloudflare R2.
  • Webhooks are a good mechanism for receiving and storing prediction output.

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