mcpField
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run-models

run-models

github.com/replicateDevelopment1442.3k installsUpdated 21w ago
Repository

About run-models

run-models is a skill that guides developers through running models with the Replicate API, from selecting a model and validating its schema to retrieving prediction results. It also covers asynchronous execution, webhooks, streaming, and chained workflows.

Key features

  1. 01Create predictions and poll for results
  2. 02Use wait responses or completion webhooks
  3. 03Validate inputs against model schemas
  4. 04Handle streaming and prediction states
  5. 05Chain models using output file URLs
  6. 06Run independent predictions concurrently

Use cases

  1. 01Running a model and collecting its output
  2. 02Receiving completed predictions through webhooks
  3. 03Building multi-model generation pipelines

MCP support

The documentation lists an MCP server reference but does not describe MCP functionality.

Install

Copy the command and run it in a terminal.

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

After installing, type/run-modelsin Claude Code to confirm it loaded.

Details

From the project README

Docs

Workflow

  1. Choose the right model - Search with the API or ask the user.
  2. Get model metadata - Fetch 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.

Three ways to get 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. Max 60 seconds.
  3. 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/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.

Predictions

  • A prediction goes through these states: starting -> processing -> succeeded / failed / canceled.
  • Official models use owner/name format. Community models require owner/name:version_id.
  • The POST /v1/predictions endpoint handles both.

Webhooks

  • Set webhook to an HTTPS URL when creating a prediction. Replicate POSTs the full prediction object when it completes.
  • Filter events with webhook_events_filter: start, output, logs, completed.
  • Validate webhook signatures using the Webhook-ID, Webhook-Timestamp, and Webhook-Signature headers. Get the signing secret from GET /v1/webhooks/default/secret.

Prediction lifetime

  • Set lifetime to auto-cancel predictions that run too long (e.g. 30s, 5m, 1h). Measured from creation time.

Streaming

  • Language models that support streaming include a stream URL in the response. Use SSE to receive incremental output.

File handling

  • Prefer HTTPS URLs for file inputs. Output URLs from one prediction can be passed directly as file inputs to the next model.
  • Output file URLs expire after 1 hour. Download and store them immediately if you need to keep them.

Multi-model workflows

  • Chain models by passing output URLs as file inputs to the next model.
  • Start all independent predictions in parallel, then collect results.
  • Output URLs are valid for 1 hour, which is enough for pipeline steps.

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