# run-models

Teaches coding agents to run AI models on Replicate via predictions, webhooks, and streaming. Useful for automating inference pipelines.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2200 installs · ★ 141
- Author: replicate
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/replicate/skills/tree/HEAD/skills/run-models/SKILL.md
- Updated: 2026-06-04
- HTML: https://affpapa.org/en/nevibecode/skills/run-models

## What it does

This skill guides coding agents through the Replicate API using the unified `POST /v1/predictions` endpoint. It covers prediction states from `starting` to `succeeded`, input validation against per-model OpenAPI schemas, and concurrent runs. For fast models, blocking synchronous responses are handled via the `Prefer: wait` header with a 60-second limit.

Long-running jobs rely on HTTPS webhooks or polling. Webhook signatures verify authenticity through standard headers, while language models expose an SSE stream URL for incremental text tokens. Independent tasks can be launched in parallel. You can also chain models by routing output URLs directly into the next step.

Output file URLs expire after 1 hour. Set up immediate backup storage like Cloudflare R2 in your workflow, because temporary files will disappear.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add replicate/skills --skill run-models -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/run-models/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/run-models/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add replicate/skills --skill run-models -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/run-models/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add replicate/skills --skill run-models -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/run-models/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install replicate/skills run-models
```

2. **Manually: put the files in place**
- File: `.github/skills/run-models/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add replicate/skills --skill run-models -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/replicate/skills --path skills/run-models
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/run-models/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add replicate/skills --skill run-models -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/replicate/skills/tree/HEAD/skills/run-models/SKILL.md

## When to use it
- Run official and community models on Replicate via API
- Validate inputs against model-specific OpenAPI schemas
- Receive completion payloads through HTTPS webhooks or polling
- Chain multiple inference steps using temporary output URLs

## Example prompts
- Run a prediction on Replicate with sdxl and send the result to my HTTPS webhook
- Fetch the OpenAPI schema for this Replicate model and validate the input parameters
- Create a Replicate pipeline where the output URL of the first model is passed to the next

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: a01378261c32c73b.

BEGIN THIRD-PARTY FILE a01378261c32c73b
```text
---
name: run-models
description: Run AI models on Replicate via predictions, webhooks, and streaming.
---

## 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.

## 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
```
END THIRD-PARTY FILE a01378261c32c73b

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