Skip to main content
Autter gives you the context behind AI-authored code without guessing from the code alone. When you need to understand a change, start with the artifacts Autter already records:
  • Line-level attribution in Git notes
  • Commit-level statistics
  • Prompt records when they are available in the active storage backend
  • Session and model identifiers for the coding agent that made the change

​
What context can show you

QuestionUse
Who wrote this line?autter blame <file>
How much of this commit came from AI?autter stats <commit-or-range>
What prompt or session created this change?autter show-prompt <id>
What does the current working tree look like?autter status
autter show-prompt only returns prompt records that exist in the current storage backend. In local-only mode, that usually means local data on the machine.

​
Use context when reviewing code

When a line looks surprising, use the surrounding authorship data first:
  1. Check the file with autter blame.
  2. Open the relevant commit with autter stats or autter show.
  3. Read the prompt record with autter show-prompt if the backend has one.
  4. Compare the result with the current repository history.
This gives you the intent behind the code, not just the final diff.

​
Keep context available to your team

Connected mode can make prompt and attribution data available to the Autter platform for shared analytics and audit history. Local-only mode keeps the same line-level attribution on the machine and in Git notes. Use it when prompts must stay off the platform.

AI blame

Inspect line-level authorship in a file.

Data and privacy

See what stays local and what can sync when you connect.