Agented

This Skill addresses the lack of history and concurrency control when AI coding agents edit files. In real-world development, AI agents often need to modify code in multiple steps, but the traditional "read-then-write" pattern can easily lead to conflicts or lost changes if the file is modified by other processes. Agented uses a state token (state_token) and a transaction mechanism to ensure every write is based on the latest state. If a conflict occurs, it automatically rejects the operation and returns the new state, allowing the AI to safely retry.

Usage is straightforward. After installing the ae binary, you operate via command-line verbs in your terminal. When first accessing a file, use ae open <path> to obtain a state token. Subsequent operations like read, replace, and insert should include the --expect <token> parameter. All edits are persisted to a SQLite workspace, forming a traceable undo tree that supports branch navigation. For multi-line content, you can pipe it via standard input to avoid complex quote escaping.

It is particularly well-suited for AI coding agents that require multi-step, cross-file editing. This includes scenarios involving complex refactoring, transactional multi-file modifications (e.g., renaming variables, migrating API calls), or teams that want to preserve a complete edit history for future review. For one-off, single modifications or scenarios where history is unnecessary, you can use the platform's built-in editor directly.

It is recommended to consistently use this tool when starting complex refactoring tasks and to enable the concurrency.require_expect: writes configuration to enforce state checks. Note that it only supports plain text files, not binary files. It also requires the Go runtime for self-compilation, or you can install a pre-compiled binary via Homebrew.