proactive-agent

A proactive AI agent anticipates user needs and initiates relevant actions autonomously, reducing manual prompting by maintaining continuity across sessions and surfacing improvements during routine heartbeats.

To make local-first agents proactive, copy the provided assets to your workspace, start the onboarding process, and enter proactive mode to enable autonomous self-improvement and memory management.

Agent memory management works by maintaining continuity across sessions using a Write-Ahead Log (WAL), a Working Buffer, and Compaction Recovery to preserve context and prevent data loss during local-first workflows.

Yes, you can enforce security hardening in AI agents by applying alignment checks to prevent prompt injection and data leakage, ensuring safe autonomous behavior during onboarding and routine operations.

Activating proactive agent skills requires a root SKILL.md file with YAML frontmatter specifying a name and description, alongside optional scripts, references, and assets folders to support the workflow.

Use cron jobs and heartbeats for agent onboarding when you need local-first agents to autonomously check for updates, onboard humans, and surface workflow improvements without continuous manual intervention.