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ai-tasks — Claude Code skill: what it does, how to install

Configures structured visual analysis rules for ImageKit. It helps generate tag lists, populate DAM metadata, and run binary quality checks.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add imagekit-developer/skills --skill ai-tasks -a claude-code
Open on GitHub

What ai-tasks does

The skill guides your agent through creating ImageKit AI Tasks configurations. Each setup includes 1 to 10 visual sub-tasks targeting images or videos. The agent frames natural-language prompts tied to one of three action types: select_tags, select_metadata, or yes_no.

Analysis relies on controlled vocabularies of 1 to 30 approved terms. For select_tags, total vocabulary length cannot exceed 500 characters, and tag values must never contain the % sign. Binary yes_no checks require at least one action under on_yes or on_no. For select_metadata, your target field must already exist in DAM or the update fails immediately.

Deploying these tasks happens through several ImageKit workflows. You can pass them through the DAM MCP server at https://imagekit.io/mcp/dam, attach them to upload API calls, or enforce them via folder path policies.

What it helps with

  • generate controlled product tags from fixed value lists
  • run automated binary quality checks on framing before publishing assets
  • map visual colors directly into ImageKit custom metadata fields
  • build upload extension payloads for the ImageKit DAM MCP server

Example prompts for the agent

  • Create an ai-tasks configuration for fashion items to detect collar type from an approved list.
  • Build a yes_no quality check that assigns needs_reshot when the product is not fully visible.
  • Write an ImageKit upload extension JSON with select_metadata for the primary_color field.

How to install ai-tasks

1Skills CLI (Vercel)

npx skills add imagekit-developer/skills --skill ai-tasks -a claude-code

2Manually: put the files in place

~/.claude/skills/ai-tasks/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/ai-tasks/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about ai-tasks

How do I install ai-tasks in Claude Code?

Run npx skills add imagekit-developer/skills --skill ai-tasks -a claude-code. The skill file installs to ~/.claude/skills/ai-tasks/SKILL.md.

What media formats does this skill handle?

It applies strictly to visual media like images and videos stored in ImageKit. It does not process non-visual document formats.

What are the restrictions on tags and vocabularies?

Approved vocabularies hold 1 to 30 items, and combined length for select_tags must stay under 500 characters. Tag values cannot contain the % character.

Source text

SKILL.md · MIT
---
name: ai-tasks
description: Apply AI-powered analysis to images for business-specific tagging, metadata extraction, and quality checks using controlled vocabularies. Use when user wants to analyze images and apply structured metadata in ImageKit.
---

# 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.
```json
{
  "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).
```json
{
  "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.
```json
{
  "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](resources/EXAMPLES.md).

© imagekit-developer · MIT · imagekit-developer/skills

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