一键在 Manus 中运行任何 Skill

开始使用

$pwd:

run-models

Name: Run Models
Author: replicate

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

在 Manus 中运行

$ git log --oneline --stat

stars:44

forks:5

updated:2026年4月17日 17:53

SKILL.md

readonly

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

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

Three ways to get output

Create a prediction, store its id from the response, and poll until completion.
Set a Prefer: wait header when creating a prediction for a blocking synchronous response. Only recommended for very fast models. Max 60 seconds.
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.

related-skills.json

同仓库

find-models.md

from "replicate/skills"

Find AI models on Replicate using search and curated collections.

2026-04-2944

prompt-images.md

from "replicate/skills"

Prompting techniques for AI image generation and editing models on Replicate. Use when writing prompts for image models or building image generation features.

2026-04-2944

prompt-videos.md

from "replicate/skills"

Prompting techniques for AI video generation models on Replicate. Use when writing prompts for video models or building video generation features.

2026-04-2944

build-models.md

from "replicate/skills"

Package and build custom AI models with Cog for deployment on Replicate. Use when creating a cog.yaml or predict.py, defining model inputs and outputs, loading model weights at setup time, building Docker images for ML models, serving locally with cog serve or cog predict, or porting a HuggingFace, GitHub, or ComfyUI model to run on Replicate. Trigger on phrases like "build a model", "package a model", "create a Cog model", "wrap a model", "containerize an AI model", "predict.py", "cog.yaml", "BasePredictor", or "Cog container", and when referencing cog.run, github.com/replicate/cog, or github.com/replicate/cog-examples. Covers GPU and CUDA setup, pget for fast weight downloads, async predictors with continuous batching, streaming outputs, and cold-boot optimization for image, video, audio, and LLM models. For pushing built models to Replicate, see publish-models. For running existing models, see run-models.

2026-04-2844

publish-models.md

from "replicate/skills"

Push and publish custom AI models to Replicate, and set up CI/CD for releasing new model versions safely. Use when running cog push, deploying a model to Replicate, releasing a new version, validating a model with cog-safe-push before publishing, configuring a Replicate deployment, setting up GitHub Actions for model releases, or porting a community model to an official one. Trigger on phrases like "push a model to Replicate", "publish a model", "deploy a model", "release a new version", "cog push", "cog-safe-push", "model CI", "r8.im", or "schema compatibility", and when referencing github.com/replicate/cog-safe-push or github.com/replicate/model-ci-template. Covers cog push, the full cog-safe-push config (test cases, fuzz, deployment, official_model), GitHub Actions patterns, multi-model matrix pushes, and post-publish monitoring. Assumes you already have a working Cog project; see build-models if you need to package one first.

2026-04-2744

compare-models.md

from "replicate/skills"

Compare Replicate models by cost, speed, quality, and capabilities.

2026-04-1744

package.json

"author": "replicate"

"repository": "replicate/skills"

打开 GitHub 仓库查看创作者相关仓库

$ install --global

$ download --local

在 Manus 中运行

$ useful --forSOC

软件开发工程师计算机与数学类职业15-1252L4

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

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

Three ways to get output

Create a prediction, store its id from the response, and poll until completion.
Set a Prefer: wait header when creating a prediction for a blocking synchronous response. Only recommended for very fast models. Max 60 seconds.
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.

run-models

Docs

Workflow

Three ways to get output

Guidelines

Predictions

Webhooks

Prediction lifetime

Streaming

File handling

Multi-model workflows

同仓库更多 Skills

同仓库更多 Skills

Docs

Workflow

Three ways to get output

Guidelines

Predictions

Webhooks

Prediction lifetime

Streaming

File handling

Multi-model workflows