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overlay-fallback

Direct users to manual annotation when automated image analysis fails.

Updated Aug 23, 2026
Search Tags:#fallback#image analysis#annotation#manual input#element detection
One-click install
npx skills add https://github.com/garimto81/claude --skill overlay-fallback
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: overlay-fallback
Source: https://github.com/garimto81/claude/tree/main/.claude/skills/overlay-fallback
Command: npx skills add https://github.com/garimto81/claude --skill overlay-fallback

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill provides a fallback mechanism when automated image analysis or overlay element detection fails, guiding users to a manual annotation tool.

Core Features & Use Cases

  • Automated Fallback: Triggers when OCR confidence is low, element detection yields no results, or specific keywords are used.
  • Manual Annotation Guidance: Directs users to the coord_picker.html tool for manual annotation.
  • Use Case: If an automated system fails to identify product labels on an image, this skill will prompt the user to open the manual annotation tool to label the elements themselves.

Quick Start

Guide the user to the manual annotation tool when element detection fails.

Frequently Asked Questions about overlay-fallback

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I handle image analysis failures when element detection returns zero results?▼

When image analysis fails or element detection returns zero results, a manual annotation fallback directs you to launch an HTML tool. This lets you manually label elements, generating output JSON paths and regenerated images yourself.

What should I do if OCR confidence is low during image analysis?▼

If OCR confidence is low during image analysis, trigger the manual annotation fallback. It guides you to open a manual coordinate picker tool to annotate the image elements and output the required JSON data.

Can I manually annotate images when automated overlay detection fails?▼

Yes, you can manually annotate images when automated overlay detection fails. The fallback mechanism directs you to a manual annotation tool to pick coordinates and save the output as JSON files.

When do I need manual input for image analysis instead of automated detection?▼

You need manual input for image analysis when automated detection fails, such as when OCR confidence is low or zero elements are detected. Specific user keywords can also trigger this fallback to open the manual annotation tool.

What output formats are generated after manual annotation of image elements?▼

After manual annotation of image elements, the process generates JSON files containing the coordinates and regenerated images. The fallback mechanism specifies the exact output paths for these generated files.

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