Agented

This Skill addresses the lack of persistent state and version history when LLMs edit code. In practical development, when an AI Agent needs to make multi-step modifications to the same file or edit across sessions, context is easily lost—each file read feels like the first time, leading to redundant work or conflicts. Agented provides LLMs with an editing experience akin to a human IDE, ensuring every edit is recorded.

Usage is straightforward: you simply operate on files via the ae command-line tool. When first encountering a file, run ae open <path>; it returns the file ID, line count, state token, and other details. Subsequent edits use ae s <file> -r <range> -w <content> --expect <token> for replacements, or ae i for insertions and ae w for writes. All operations are persisted in SQLite, supporting an undo tree and branching, enabling historical state recovery even across sessions.

It is ideal for AI Agent development scenarios involving frequent code edits, especially for multi-step refactoring or cross-session collaboration, whether in teams or individually. It also fits well in CI/CD pipelines for automated file modifications with audit trails, or as a core editor for LLM coding assistants, ensuring every write is safe and reliable.

It is recommended to consistently use ae open when an AI Agent first accesses a file; do not skip this step. Note that it only supports text files, not binary files. For one-off, single edits, using the platform's built-in editor can save tokens. Additionally, multi-line content is best passed via pipe or the -f parameter to avoid complex quote escaping.