Documentation

What onplate is, how to install it on macOS, Linux and Windows, and what you see on the first launch.

From an empty storage to your first skill installed into your AI tool — in five minutes.

The overview screen - a map of your AI system with context weight, what needs attention right now, usage over 30 days and a resource summary.

The main section - every skill and package you have. Adding, installing into AI tools, sorting, tags, favourites, bulk actions, the quality profile and the trash.

Bundles of skills, commands and MCP servers - adding from a folder or a Git repository, installing as loose resources or as a native plugin, exporting to a foreign format, updating and rolling back.

Connecting MCP servers - by hand, from JSON or from a registry. Secrets are encrypted, the connection is tested before install.

Subagents for your AI - a narrow job, their own tool set and permissions. Creating, importing, security checks and installing into AI tools.

Editing skills with a working copy, publishing versions, changelogs, comparing versions and rolling back.

Add a code folder and onplate shows what the AI actually gets there, how that folder differs from the one next to it, and where the work really happened.

The .onplate/profile.json file in a code repository - which skills, MCP servers and subagents this project needs. Discussed in a pull request, applied on a button.

Searching for skills and MCP servers on external platforms, a preview with a security check, and import into your storage.

Connected Git sources with skills, packages and subagents - update tracking and re-import.

The skill scanner - prompt injection, dangerous commands, hidden unicode. Scores, working with findings and trusted domains.

Finding contradictions between skill instructions and collisions between MCP servers - with the reason and advice on what to do.

The install map - which skill, MCP server or subagent, in which AI, which version and at which path.

Scanning the system - finding skills installed straight into an AI and taking them under onplate management.

The history of every operation - installs, updates, rollbacks and removals. What, when and where.

The Team section - three ways to hand your setup to other people: a team profile from a Git registry, a project profile in the code repository, and a set as a single file.

Appearance, language, window, behavior and the skill install paths for every AI.

The local helper for the onplate interface - explains the screen, the consequences of actions, errors and confirmed results.

Trying a skill for real - onplate hands it to your AI on an ordinary task and reads the session log to show whether the model reached for it.