Awesome-claude-skills-cn Replicate Automation

通过 Composio MCP 集成自动化 Replicate AI 模型操作——运行预测、上传文件、检查模型架构、列出版本并管理预测历史。

install

source · Clone the upstream repo

git clone https://github.com/Athe1st3154/awesome-claude-skills-cn

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/Athe1st3154/awesome-claude-skills-cn "$T" && mkdir -p ~/.claude/skills && cp -r "$T/composio-skills/replicate-automation" ~/.claude/skills/athe1st3154-awesome-claude-skills-cn-replicate-automation && rm -rf "$T"

manifest: composio-skills/replicate-automation/SKILL.md

source content

Replicate Automation

Automate your Replicate AI model workflows -- run predictions on any public model (image generation, LLMs, audio, video), upload input files, inspect model schemas and documentation, list model versions, and track prediction history.

Toolkit docs: composio.dev/toolkits/replicate

设置

Add the Composio MCP server to your client:
```
https://rube.app/mcp
```
Connect your Replicate account when prompted (API token authentication)
Start using the workflows below

Core Workflows

1. Get Model Details and Schema

Use

REPLICATE_MODELS_GET

to inspect a model's input/output schema before running predictions.

Tool: REPLICATE_MODELS_GET
Inputs:
  - model_owner: string (required) -- e.g., "meta", "black-forest-labs", "stability-ai"
  - model_name: string (required) -- e.g., "meta-llama-3-8b-instruct", "flux-1.1-pro"

Important: Each model has unique input keys and types. Always check the

openapi_schema

from this response before constructing prediction inputs.

2. Run a Prediction

Use

REPLICATE_MODELS_PREDICTIONS_CREATE

to run inference on any model with optional synchronous waiting and webhooks.

Tool: REPLICATE_MODELS_PREDICTIONS_CREATE
Inputs:
  - model_owner: string (required) -- e.g., "meta", "black-forest-labs"
  - model_name: string (required) -- e.g., "flux-1.1-pro", "sdxl"
  - input: object (required) -- model-specific inputs, e.g., { "prompt": "A sunset over mountains" }
  - wait_for: integer (1-60 seconds, optional) -- synchronous wait for completion
  - cancel_after: string (optional) -- max execution time, e.g., "300s", "5m"
  - webhook: string (optional) -- HTTPS URL for async completion notifications
  - webhook_events_filter: array (optional) -- ["start", "output", "logs", "completed"]

Sync vs Async: Use

wait_for

(1-60s) for fast models. For long-running jobs, omit it and use webhooks or poll via

REPLICATE_PREDICTIONS_LIST

3. Upload Files for Model Input

Use

REPLICATE_CREATE_FILE

to upload images, documents, or other binary inputs that models need.

Tool: REPLICATE_CREATE_FILE
Inputs:
  - content: string (required) -- base64-encoded file content
  - filename: string (required) -- e.g., "input.png", "audio.wav" (max 255 bytes UTF-8)
  - content_type: string (default "application/octet-stream") -- MIME type
  - metadata: object (optional) -- custom JSON metadata

4. Read Model Documentation

Use

REPLICATE_MODELS_README_GET

to access a model's README in Markdown format for detailed usage instructions.

Tool: REPLICATE_MODELS_README_GET
Inputs:
  - model_owner: string (required)
  - model_name: string (required)

5. List Model Versions

Use

REPLICATE_MODELS_VERSIONS_LIST

to see all available versions of a model, sorted newest first.

Tool: REPLICATE_MODELS_VERSIONS_LIST
Inputs:
  - model_owner: string (required)
  - model_name: string (required)

6. Track Prediction History and Files

Use

REPLICATE_PREDICTIONS_LIST

to retrieve prediction history, and

REPLICATE_FILES_GET

REPLICATE_FILES_LIST

to manage uploaded files.

Tool: REPLICATE_PREDICTIONS_LIST
  - Lists all predictions for the authenticated user with pagination

Tool: REPLICATE_FILES_LIST
  - Lists uploaded files, most recent first

Tool: REPLICATE_FILES_GET
  - Get details of a specific file by ID

已知陷阱

Pitfall	Detail
Model-specific input keys	Each model has unique input keys and types. Using the wrong key causes validation errors. Always call `REPLICATE_MODELS_GET` first to check the `openapi_schema` .
File upload encoding	`REPLICATE_CREATE_FILE` requires base64-encoded content. Binary files treated as text (UTF-8) will fail with decode errors.
Public vs deployment paths	Public models must be run via `REPLICATE_MODELS_PREDICTIONS_CREATE` . Using deployment-oriented paths causes HTTP 404 failures.
Sync wait limits	`wait_for` supports 1-60 seconds only. Long-running jobs need async handling via webhooks or polling `REPLICATE_PREDICTIONS_LIST` .
Image model constraints	Image models like flux-1.1-pro have specific constraints (e.g., max width/height 1440px, valid aspect ratios). Check the model schema first.
Stale file references	Heavy usage creates many uploads. Routinely check `REPLICATE_FILES_LIST` to avoid using stale `file_id` references.

快速参考

Tool Slug	Description
`REPLICATE_MODELS_GET`	Get model details, schema, and metadata
`REPLICATE_MODELS_PREDICTIONS_CREATE`	Run a prediction on a model
`REPLICATE_CREATE_FILE`	Upload a file for model input
`REPLICATE_MODELS_README_GET`	Get model README documentation
`REPLICATE_MODELS_VERSIONS_LIST`	List all versions of a model
`REPLICATE_PREDICTIONS_LIST`	List prediction history with pagination
`REPLICATE_FILES_LIST`	List uploaded files
`REPLICATE_FILES_GET`	Get file details by ID

由 Composio 提供支持