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    ComfyUI Media Engine Skill

    1

    Install, configure, and automate ComfyUI for image, video, and audio generation via API and custom workflows.

    $5

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    ComfyUI Media Engine Skill

    ComfyUI Media Engine Skill

    Example session with this skill installed

    Create a JSON workflow for a basic Stable Diffusion XL image generation task and provide the Python code to submit it to a local ComfyUI API on port 8188.

    • Read your context and instructions
    • Compiled the comfyui media engine

    The following deliverable includes a standard SDXL API-format JSON and a Python script for job submission.

    ComfyUI API Workflow

    {
  "3": {
    "class_type": "KSampler",
    "inputs": {
      "cfg": 8,
      "denoise": 1,
      "latent_image": ["5", 0],
      "model": ["4", 0],
      "negative": ["7", 0],
      "positive": ["6", 0],
      "sampler_name": "euler",
      "scheduler": "normal",
      "seed": 8566357,
      "steps": 20
    }
  },
  "4": {
    "class_type": "CheckpointLoaderSimple",
    "inputs": {
      "ckpt_name": "sd_xl_base_1.0.safetensors"
    }
  },
  "5": {
    "class_type": "EmptyLatentImage",
    "inputs": {
      "batch_size": 1,
      "height": 1024,
      "width": 1024
    }
  },
  "6": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["4", 1],
      "text": "cinematic shot, mountain landscape, sunset, 8k"
    }
  },
  "7": {
    "class_type": "CLIPTextEncode",
    "inputs": {
      "clip": ["4", 1],
      "text": "blurry, low quality, distorted"
    }
  },
  "8": {
    "class_type": "VAEDecode",
    "inputs": {
      "samples": ["3", 0],
      "vae": ["4", 2]
    }
  },
  "9": {
    "class_type": "SaveImage",
    "inputs": {
      "filename_prefix": "api_gen",
      "images": ["8", 0]
    }
  }
}

    import json
import urllib.request

def queue_prompt(prompt):
    p = {"prompt": prompt}
    data = json.dumps(p).encode('utf-8')
    req = urllib.request.Request("http://127.0.0.1:8188/prompt", data=data)
    with urllib.request.urlopen(req) as response:
        return json.loads(response.read())

# Example usage with the JSON above
with open('workflow_api.json', 'r') as f:
    workflow = json.load(f)
    
result = queue_prompt(workflow)
print(f"Prompt queued: {result['prompt_id']}")

    Next steps

    • Ensure sd_xl_base_1.0.safetensors is in the models/checkpoints folder.
    • Start ComfyUI with the --listen 127.0.0.1 flag.
    • Verify the port matches your instance if not using 8188.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Managing a ComfyUI environment for local or remote AI media generation is often a brittle process of dependency hell, missing nodes, and VRAM management. This skill provides a structured approach for technical assistants to install, configure, and automate ComfyUI across image, video, and audio pipelines. It bridges the gap between raw Python environments and production-ready API workflows.

    What it does

    • Environment management handles OS-specific installation, Python virtual environments, and hardware-specific launch commands.
    • Model orchestration manages checkpoints, VAEs, LoRAs, and motion models within the correct folder structures.
    • Dependency resolution audits custom nodes, installs required Python packages, and manages version compatibility.
    • Workflow engineering generates and validates ComfyUI JSON files for both UI and API consumption.
    • API automation handles job queuing, status monitoring, and output retrieval through the ComfyUI server.

    How it works

    1. Discover the hardware, VRAM, and existing installation paths to establish a baseline.
    2. Setup the server using platform-specific commands and isolated environments.
    3. Configure required models and custom nodes based on the target media type.
    4. Deploy a validated JSON workflow to the API or UI for generation.

    Frameworks & tools

    This skill works with ComfyUI, Python, CUDA/ROCm, and standard AI media formats. It interfaces with the ComfyUI API, ComfyUI Manager, and various custom node ecosystems for Stable Diffusion and motion models.

    Why this beats prompting it yourself

    Generic prompts often hallucinate node names, download links, and JSON structures that don't match the actual ComfyUI environment. This skill enforces a diagnostic-first workflow that verifies hardware constraints and node schemas before suggesting code. It prevents broken environments by prioritizing isolated dependencies and verified repository instructions.

    Use cases

    • Automate high-resolution image generation pipelines via API.
    • Set up a video generation environment using AnimateDiff or SVD.
    • Troubleshooting CUDA memory errors or missing custom node dependencies.
    • Migrating a manual UI workflow into a headless API script.

    Known limitations

    Does not bypass license requirements for restricted models. Performance is strictly limited by the user's available GPU VRAM and disk space. API features require a running ComfyUI server instance.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Requires

    Python

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