Agent skill · software engineering · replicate
run-models
Run AI models on Replicate via predictions, webhooks, and streaming.
Why this skill is useful
Provides specific commands and workflows for interacting with the Replicate API, including handling predictions and webhooks that the AI wouldn't reliably generate on its own.
What it needs
Requires replicate account access. About 2k tokens when loaded. Last updated 2026-06-04. 53 stars on the source repository.
What this skill does
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/markdown when requesting docs pages for Markdown responses. 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills replicate/run-models