Vision Ocr

Overview

Extracting structured text coordinate boundaries from screenshots. This deep-dive module provides advanced prompt engineering and systemic context rules.

When to Use

• When building production-ready cognitive loops requiring high logical alignment.
• When standard models drift or ignore simple constraints.

When NOT to Use

• For simple one-off tasks where speed and low latency are prioritized.

Best Practices

• Define Explicit Goals: Structure success boundaries before generating inputs.
• Negative Enclosures: Wrap forbidden patterns inside <not_allowed> tags.
• AST Checks: Validate code formatting prior to test execution.

Common Mistakes

• Premature Execution: Letting the model edit files without creating an implementation plan.
• Over-Abstraction: Creating abstract interfaces where single functions would suffice.

Advanced Techniques

• Dynamic Context Injection: Injecting variables dynamically based on prompt length constraints.
• Self-Healing AST: Interfacing compiler stack traces directly to model correction prompts.

Prompt Template

<instructions>
You are executing the VISION-OCR skill. 
Analyze the task below, write an implementation plan, and verify all constraints.
</instructions>
<task>
{{task_description}}
</task>

Production Use Case

• Automated local refactoring pipelines correcting linter errors autonomously in CI/CD cycles.

Evaluation Checklist

• Model states assumptions explicitly.
• No speculative helper functions are written.
• Output formatting passes lint validation check.