Agented

This Skill addresses the lack of history and concurrency control when LLMs edit files. In real-world development, AI agents often need to modify the same file multiple times, but standard editors cannot track each version of these edits, easily leading to conflicts or lost work. Agented ensures every edit is traceable and reversible through its stateful editor and undo tree, supporting branch navigation.

Usage is straightforward: after installing the ae binary, run ae open <file> in the terminal to open a file and obtain a state_token. You can then perform operations like replacement and insertion using short-form commands such as ae s <file> -r <range> -w <text> -x <token>. All modifications are persisted to a SQLite database, with support for transactions and concurrency detection. If the file is modified by another process, writes will fail and return the new content and token, allowing for safe retries.

It is particularly well-suited for AI agent development scenarios involving multi-step edits, especially for teams or individuals needing to batch-refactor code or manage changes across multiple files. For developers already using the platform's built-in editor, Agented provides more robust history and concurrency safety guarantees, making it ideal for performing secure refactoring operations in complex codebases.

In multi-agent collaboration scenarios, it is recommended to uniformly set require_expect: writes to enforce pre-write checks and avoid concurrency conflicts. Note that it primarily targets text files and does not support binary files, requiring local installation of the ae binary. For single, simple edits, using the platform's built-in editor might be more lightweight.