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ai-tasks

ai-tasks

github.com/imagekit-developerData & databases2482 installsUpdated 3w ago
Repository

About ai-tasks

ai-tasks is a skill for defining image and video analysis tasks, aimed at teams that need consistent tags, metadata, or visual quality checks. It structures instructions as selectable vocabularies or yes/no actions.

Key features

  1. 01Tag selection from approved values
  2. 02Metadata extraction into DAM fields
  3. 03Binary checks with conditional tag actions
  4. 04Task configuration for visual content

Use cases

  1. 01Categorizing product images by business terms
  2. 02Checking whether products are fully visible
  3. 03Extracting image attributes into DAM metadata

MCP support

AI Tasks can be managed through the DAM MCP endpoint using `list_saved_extensions`, `create_saved_extension`, and `apply_extension_bulk`.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/imagekit-developer/skills --skill ai-tasks

After installing, type/ai-tasksin Claude Code to confirm it loaded.

Bundled filesresources/EXAMPLES.md

Details

From the project README

AI Tasks Skill

When to use

  • User wants to tag images with business-specific categories
  • User needs controlled vocabularies (predefined value lists)
  • User wants yes/no quality checks on images
  • User needs to extract metadata using natural language analysis

AI Task Structure

Each AI Task contains 1-10 sub-tasks with:

  1. Instruction (required): Natural language question about the image
  2. Action Type (required): select_tags, select_metadata, or yes_no
  3. Vocabulary (optional): 1-30 predefined approved values

Action Types

select_tags

Adds tags from vocabulary. Supports multiple selections.

{
  "type": "select_tags",
  "instruction": "What body style is this vehicle?",
  "vocabulary": ["sedan", "suv", "hatchback"],
  "max_selections": 1
}
select_metadata

Sets custom metadata field (field must exist in DAM).

{
  "type": "select_metadata",
  "instruction": "What is the primary color?",
  "field": "primary_color",
  "vocabulary": ["red", "blue", "white", "black"],
  "max_selections": 1
}
yes_no

Binary quality check with conditional actions.

{
  "type": "yes_no",
  "instruction": "Is the product completely visible?",
  "on_yes": { "add_tags": ["framing_ok"] },
  "on_no": { "add_tags": ["needs_reshot"] }
}

Gotchas

  • Instruction clarity: Be specific ("What is the collar type?" not "Describe the image")
  • Vocabulary design: Use business terminology, non-overlapping, 1-30 items max
  • Field requirements: For select_metadata, field must exist in DAM first
  • Tag values: Cannot contain % character
  • yes_no tasks: Must have at least one of on_yes or on_no
  • Vocabulary length: Max 500 characters combined (select_tags only)
  • Scope: Works on visual content only (images/videos)

Applying AI Tasks

  • Via DAM MCP: list_saved_extensions, create_saved_extension, and apply_extension_bulk on https://imagekit.io/mcp/dam
  • Via Saved Extensions: Create and apply via dashboard/API
  • Via API at Upload: Include in extensions array
  • Via Path Policies: Auto-apply to files in specific folders

Full examples

For complete, copy-ready ai-tasks configurations organized by industry (fashion e-commerce, travel, automotive) and detailed per-task-type parameter references, read resources/EXAMPLES.md.

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