Recommendations are actionable directives generated from findings. Every recommendation is tied to a specific finding (Documentation Index
Fetch the complete documentation index at: https://docs.ironbee.ai/llms.txt
Use this file to discover all available pages before exploring further.
concern or critical severity) and grounded in observed session data.
What makes recommendations different
Recommendations are not generic advice — they are imperative instructions written specifically for the AI coding agent. They are automatically injected into the agent’s system prompt on the next session, so the agent adjusts its behavior without you needing to manually relay the feedback. Example recommendations:- “Always check contrast ratio, form validation, and error states before writing a verdict.” (quality finding)
- “After a fix, re-run all checks — not just the failing one.” (efficiency finding)
- “Button.tsx has a 70% fail rate — take extra care when modifying this file.” (patterns finding)
- “Prefer prompts with stable cached prefixes when working in src/auth/ — current cache hit rate is below 30%.” (cost finding)
Recommendation scope
| Scope | Injected when |
|---|---|
| Project-scoped | Agent works in any session for that project |
| Account-scoped | Agent works in any project for that account |
Recommendations list
The Recommendations tab shows all active recommendations, linked to their source findings. Each recommendation includes:- Action — what the agent should do, written as a clear directive
- Source finding — the observation that generated it (title, area, severity)
- Status — active or dismissed