skills/writing-and-planning/copywriting/document-editorial/composio-skills/replicate-automation/SKILL.md
Automate Replicate AI model operations -- run predictions, upload files, inspect model schemas, list versions, and manage prediction history via the Composio MCP integration.
npx skillsauth add lunartech-x/superpowers Replicate AutomationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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
https://rube.app/mcpUse 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.
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.
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
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)
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)
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
| 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. |
| 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 |
Powered by Composio
tools
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
testing
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
development
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
development
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.