AgentHandover Transforms Daily Actions Into AI Agent Skills

By vramkickedin | | 2 min read
A 3D render featuring a computer mouse resting on a smooth white surface emitting a faint glowing stream of soft blue digital particles.

AgentHandover observes daily computer activity on macOS and automatically converts repetitive routines into structured instruction files that artificial intelligence agents can follow. The system tracks mouse movements, application states, and keyboard inputs to capture the exact sequence of your workflow without requiring manual documentation.

Creator Sandroandric released the project to eliminate the constant need for prompt engineering when delegating routine tasks. Instead of writing step-by-step guides, users simply perform their usual work while the software learns the underlying strategy and decision logic behind every action.

Automated workflow extraction and execution

  • Records active tasks through focused recording sessions or passive background monitoring.
  • Generates skill files containing steps, selection rules, guardrails, and personal writing style.
  • Improves accuracy over time by tracking execution success and adjusting confidence scores.
  • Connects to compatible agents through single-command setups or automatic menu bar detection.

Professionals managing repetitive digital routines can remove the manual documentation phase from their automation stack. The tool captures nuanced decision patterns that standard prompt templates typically miss, allowing AI assistants to replicate complex processes with contextual awareness.

Privacy focused local processing architecture

The entire intelligence pipeline operates on-device, using visual language models to analyze screen frames without sending sensitive data to remote servers.

"I built it because every time I wanted an agent to handle something for me I had to explain the whole process from scratch, even for stuff I do daily,"

noted the creator in a post. Screenshots are immediately processed, redacted for personal data, and converted into structured text before the temporary images are permanently deleted.

Automating routine digital workflows requires only a brief initial recording before agents take over the execution. You can review the AgentHandover project on GitHub to begin integrating local observation into your daily operations.