Help

The prompt editor opens when you click the pencil icon next to any prompt on the Edit Prompts screen.


The Editor

Prompts are edited in a syntax-highlighted code editor. Prompt substitution tags (e.g. {{prompt:Tone}}) are highlighted so they stand out from plain text.

Hover over a substitution tag to preview the content that will be injected when the agent runs.


Prompt Substitutions

Prompt substitutions let you embed a Prompt Component's content directly into an agent prompt at runtime, keeping instructions consistent and avoiding duplication.

Use the Insert prompt substitution dropdown to insert a tag at the cursor position. The dropdown lists all available components with a preview on hover. The tag takes the form {{prompt:Component Name}}.


Saving

When you click Save Prompt, a dialog asks you to enter a commit message describing your change. The system AI will suggest a message automatically — you can edit it or replace it entirely before confirming. Every save is version-controlled so changes can be reviewed and restored.


AI Prompt Review (Beta)

Click Review Prompt Changes with AI in the save bar to open a side panel that analyses your changes and gives feedback on clarity, completeness, and potential issues. This runs against the diff between the saved version and your current edits.


Version History

Click View History to open the version history modal. This shows a chronological list of every saved version of the prompt, including the date, author, and commit message.

Click any version to view its full content. You can switch between Content (read-only view of that version) and Diff with Current (a line-by-line comparison against what is currently in the editor).

Click Restore This Version to load the selected version into the editor. This does not save immediately — you will still need to click Save Prompt to apply the restored version.


Prompt Writing Tips

The editor includes a set of tips below the text area:

  • Be specific and clear — State exactly what you want the agent to do. Avoid vague language.
  • Provide context — Include relevant background, tone, format expectations, and examples.
  • Break complex tasks into steps — For multi-step tasks, guide the model through each step systematically.

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