smart file screenshot action assistant

The problem

Users often drop screenshots, messy logs, or unformatted files into an LLM without providing context or instructions. Manual prompt engineering for every receipt, error message, or handwritten note is a friction point that slows down development and operations.

What it does

• Identifies input types including screenshots, PDF tables, stack traces, and unstructured notes automatically.
• Determines the most logical next step like fixing a bug, extracting data, or drafting a reply without user guidance.
• Converts visual or messy data into structured formats like Markdown, JSON, or checklists.
• Surfaces the "what matters" core signal from noisy inputs to save reading time.
• Executes high-value actions such as comparing versions, verifying calculations, or explaining complex jargon.

Why this beats prompting it yourself

Instead of writing "extract the dates from this photo and format as a table," you simply drop the file. The skill handles the classification and intent detection, eliminating the need for repetitive instructional prompts. It prevents "hallucination by omission" by asking exactly one targeted question only when a critical variable is missing.

Use cases

• Turn a screenshot of a terminal error into a code fix and root-cause analysis.
• Convert a photo of a whiteboard session into a structured project plan or task list.
• Extract key terms and action items from a long PDF contract or email thread.
• Compare two screenshots of UI components to identify visual discrepancies.

Known limitations

For extremely large inputs, the skill focuses on high-signal sections rather than full processing. It requires legible text in images for accurate transcription.