replicate/skills

run-models

Run AI models on Replicate via predictions, webhooks, and streaming.

All-time #7324 Trending #7698 First seen Apr 21, 2026
8-week activity · all time api

Installation

$ npx skills add replicate/skills --skill run-models

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 57
License LICENSE
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,618 B
  • docs SUMMARY.md 3,578 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1,600 installs

SKILL.md

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 webhookeventsfilter: 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.