AgentHandover

The tool runs on macOS and monitors a user’s desktop activity, turning observed workflows into structured “Skills” that capture strategy, timing, guardrails and voice style. It builds a local vector store of semantic embeddings, links sessions by meaning, and continuously refines the extracted behavior through execution feedback. Each Skill is a self‑improving playbook that can be invoked by compatible AI agents.

Developers, marketers, or any professionals who repeatedly perform the same multi‑step tasks can use it to automate those processes. By watching a user complete actions such as browsing Reddit, scanning posts, and composing replies, the system learns the decision criteria and tone, then encodes them for reuse. The resulting Skills are not static scripts; they adapt as agents report successes or deviations, automatically adjusting confidence and branching logic.

The platform offers an 11‑stage pipeline that includes local visual‑language‑model annotation, activity classification, semantic search, and behavioral synthesis, all performed locally without a screen‑recorder overlay. It can connect to a range of agents that support the MCP protocol, enabling those agents to execute the learned Skills and improve over time.