muapi-workflow

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Build, run, and visualize multi-step AI generation workflows. The AI architect translates natural language descriptions into connected node graphs — chain image generation, video creation, enhancement, and editing into automated pipelines.

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Sourcesamuraigpt/generative-media-skills
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NPX Install

npx skill4agent add samuraigpt/generative-media-skills muapi-workflow

Tags

Translated version includes tags in frontmatter
ai-workflow-buildermuapi-clipipeline-automationworkflow-visualizationagent-guided-selection

SKILL.md Content

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AI Workflow Builder

Chain any combination of muapi.ai generation steps into automated pipelines. The AI architect converts your plain-language description into a runnable node graph.

Prerequisites

Always ensure the 
muapi
CLI is up-to-date before running any commands. Reinstall from source at the start of every session:
bash
pip install --upgrade /path/to/muapi-cli
Replace 
/path/to/muapi-cli
with the actual path to the 
muapi-cli
directory in the workspace (the directory containing 
pyproject.toml
).

Core Operations

  1. Generate (
    generate-workflow.sh
    ) — AI architect creates a workflow from a description
  2. Discover (
    discover-workflow.sh
    ) — Find a relevant existing workflow by natural language
  3. Edit (
    generate-workflow.sh --workflow-id
    ) — Modify an existing workflow with a prompt
  4. Interactive Run (
    interactive-run.sh
    ) — Prompt for inputs and execute a workflow
  5. Run (
    run-workflow.sh
    ) — Execute a workflow, poll node-by-node, collect outputs
  6. CLI (
    muapi workflow
    ) — Full CRUD + visualization directly from the terminal

Agent Guided Discovery & Selection

As an AI agent, you have the ability to read and understand the purpose of available workflows to select the best one for the user's task (e.g., "create a UGC video").
  1. Discover: Fetch the catalog of available workflows and their descriptions in JSON format.
    bash
    muapi workflow discover --output-json
  2. Match (Internal Reasoning): Use your LLM capabilities to analyze the 
    name
    , 
    category
    , and 
    description
    fields of the returned workflows. Find the best match for the user's intent.
  3. Analyze: If you find a promising candidate, inspect its structure to ensure it has the necessary nodes and parameters.
    bash
    muapi workflow get <workflow_id>
    CRITICAL RULE: The output of 
    muapi workflow get
    will include an "API Inputs" table. You MUST read this table to understand what inputs are required.
  4. Choose & Confirm & Prompt User:
    • If one workflow is a perfect match, you MUST ask the user to provide the exact values for the required API inputs before executing it. Never invent or guess input values (like prompts, URLs, etc.) on your own.
    • If multiple workflows are highly relevant, present the options to the user with their descriptions and ask them to confirm which one to use, and also ask for the required inputs.
    • If no workflow matches the user's complex request, offer to architect a new one using 
      muapi workflow create
      .

Example Agent Reasoning

"The user wants a product promo video. I fetched the catalog using 
discover
. I see two potential workflows:
  1. wf_123
    : 'Product promo with background music'
  2. wf_456
    : 'Simple video gen' I will analyze 
    wf_123
    with 
    get
    . It has the required nodes. I will suggest 
    wf_123
    or just run it if the match is precise."

Protocol: Building a Workflow

Step 1 — Describe your pipeline

bash
muapi workflow create "take a text prompt, generate an image with flux-dev, then upscale it to 4K"
The architect returns a workflow with a unique ID and a node graph. Save the ID.

Step 2 — Inspect and visualize

bash
# Rich ASCII node graph in the terminal
muapi workflow get <workflow_id>

# Or raw JSON
muapi workflow get <workflow_id> --output-json

Step 3 — Run it

bash
# Run with specific inputs
muapi workflow execute <workflow_id> \
  --input "node1.prompt=a glowing crystal cave at midnight"

# Use --download to pull results locally
muapi workflow execute <workflow_id> \
  --input "node1.prompt=a sunset" \
  --download ./outputs

Step 4 — Discovery (Optional)

If you want to reuse an existing workflow instead of creating a new one:
bash
# Search by keywords
muapi workflow discover "ugc video"

Step 5 — Interactive Execution

Run a workflow and have the CLI prompt you for each required input:
bash
muapi workflow run-interactive <workflow_id>

Workflow Examples

Image Pipelines

bash
# Text → Image → Upscale
muapi workflow create "take a text prompt, generate with flux-dev, upscale the result"

# Text → Image → Background removal → Product shot
muapi workflow create "generate a product image with hidream, remove background, create professional product shot"

Video Pipelines

bash
# Text → Video
muapi workflow create "generate a 10-second cinematic video from a text prompt using kling-master"

# Image → Video → Lipsync
muapi workflow create "animate an input image with seedance, then apply lipsync from an audio file"

Editing an Existing Workflow

bash
# Add a step
muapi workflow edit <id> --prompt "add a face-swap step after the image generation"

# Swap a model
muapi workflow edit <id> --prompt "change the video model from kling to veo3"

CLI Reference

bash
# List all your workflows
muapi workflow list

# Browse templates
muapi workflow templates

# Generate new workflow
muapi workflow create "text → flux image → upscale → face swap"

# Visualize a workflow
muapi workflow get <id>

# Execute with inputs
muapi workflow execute <id> --input "node1.prompt=a sunset"

# Monitor a run
muapi workflow status <run_id>

# Get outputs
muapi workflow outputs <run_id> --download ./results

# Edit with AI
muapi workflow edit <id> --prompt "add lipsync at the end"

# Rename / delete
muapi workflow rename <id> --name "Product Pipeline v2"
muapi workflow delete <id>

MCP Tools (for AI agents)

ToolDescription
muapi_workflow_list
List user's workflows
muapi_workflow_create
AI architect: prompt → workflow
muapi_workflow_get
Get workflow definition + node graph
muapi_workflow_execute
Run with specific inputs
muapi_workflow_status
Node-by-node run status
muapi_workflow_outputs
Final output URLs

Constraints

  • Workflows can contain any combination of muapi.ai nodes (image, video, audio, enhance, edit)
  • Node outputs are automatically wired as inputs to downstream nodes
  • --sync
    mode waits up to 120s for generation; use 
    --async
    for complex workflows and poll separately
  • Run timeouts: 10 minutes maximum per workflow execution