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Documentation Index

Fetch the complete documentation index at: https://docs.nekohub.fengying.xin/llms.txt

Use this file to discover all available pages before exploring further.

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Current workflow capabilities

NekoHub already implements both workflow configuration and workflow execution:
  • Workflow profile management: create, update, delete, and inspect workflow profiles from /workflows or /api/v1/system/workflows
  • Workflow execution:
    • after a new asset is uploaded, the backend automatically queues the workflow marked as autoRun
    • from an asset detail page, you can manually trigger POST /api/v1/assets/{id}/workflows/{workflowId}/run
The workflow graph supports draggable nodes and visual edges, but the backend execution model is still linear and node-order-based. Edges are mainly for visualizing intent and order; they do not currently create branching or parallel execution semantics.

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Prerequisites

  • You need settings.read to open /workflows
  • You need settings.update to create, modify, delete, or mark a workflow as auto-run
  • If a workflow contains ai-caption, an active AI provider profile must exist or that step will fail
  • Workflows currently target image assets; the backend validates whether each skill is supported for the current asset before queueing

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Manage workflows in the admin console

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Open the workflow editor

  1. Sign in to the admin console
  2. Open /workflows
  3. Drag skills from the Available skills palette onto the canvas
  4. Set the workflow name, description, and auto-run state
  5. Save the workflow for reuse

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Currently available nodes

Based on the frontend skill catalog and backend skill resolver, the current workflow editor exposes these nodes:
NodeskillIdPurpose
ThumbnailthumbnailGenerate a thumbnail suitable for list and detail previews
AI captionai-captionGenerate descriptive text with the currently active AI provider
Strip Exifexif-stripRemove Exif metadata from the original image
Format convertformat-convertConvert the image format, with configurable output format and original retention
WatermarkwatermarkDraw a text watermark with configurable opacity, size, and position

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Current configurable parameters

Not every node has extra parameters. The frontend currently supports parameter editing for these nodes:

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format-convert

  • TargetFormat
    • supported values: webp, jpeg, png, gif, bmp, tga, tiff
  • KeepOriginal
    • whether to keep the original file

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watermark

  • Text
  • Opacity
  • FontSize
  • Position
    • supported values: BottomRight, BottomLeft, Center, TopRight, TopLeft

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Auto-run workflows

A workflow profile can be marked with isAutoRun=true. Current behavior:
  • only one auto-run workflow can exist at a time
  • when you mark one workflow as auto-run, the backend clears isAutoRun on the others
  • when an asset is uploaded with runEnrichment=true, the backend:
    1. checks whether an auto-run workflow exists
    2. if one exists, parses its nodes in saved order and queues them
    3. otherwise falls back to the default post-upload skill set
Auto-run workflows still reuse the upload-time runEnrichment switch. If you upload with runEnrichment=false, the auto-run workflow is skipped as well.

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Run a workflow manually from an asset detail page

The asset detail page loads the workflow list and prefers the auto-run workflow by default. If none is marked as auto-run, it falls back to the first workflow in the list. Manual runs are useful when:
  • an asset was uploaded without automatic processing
  • you want to rerun an existing asset with a different workflow
  • you changed a workflow and want to apply it to existing assets
Call: 
POST /api/v1/assets/{id}/workflows/{workflowId}/run
Required permission: assets.update The endpoint returns 202 Accepted, which means the workflow was queued rather than completed synchronously in the current HTTP request.

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Minimal workflow graph JSON

The backend requires graphJson to be a valid JSON object with at least a nodes array. Nodes prefer data.skillId as the executable skill identifier and only fall back to the legacy type field when data.skillId is missing. Minimal example: 
{
  "nodes": [
    {
      "id": "node-1",
      "data": {
        "skillId": "thumbnail"
      }
    },
    {
      "id": "node-2",
      "data": {
        "skillId": "ai-caption"
      }
    }
  ],
  "edges": [],
  "viewport": {
    "x": 0,
    "y": 0,
    "zoom": 1
  }
}

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Known boundaries

  • execution is still linear; there is no real branching, conditional logic, or parallel execution yet
  • ai-caption depends on an active AI provider profile
  • the backend validates skill compatibility before queueing; unsupported skills for the current asset type are rejected
  • if graphJson cannot be parsed, the frontend editor falls back to an empty canvas and shows a warning