What problem does it solve? Symbol-sensitive coding work requires accurate semantic understanding of declarations, references, and type hierarchies, which plain text search cannot provide. This Skill standardizes how an AI agent connects to Serena MCP with the JetBrains language backend, verifies the integration is actually working, and falls back safely when it is degraded. ## Core Features & Use Cases - Readiness Verification: Activates the project, checks configuration, and runs a real semantic smoke test to classify the integration as READY, DEGRADED, or UNAVAILABLE before relying on it. - Semantic Exploration and Editing: Uses JetBrains-backed declarations, references, implementations, inspections, and refactorings such as rename, move, inline, and safe delete for symbol-sensitive changes. - Integration Diagnosis: Verifies each layer separately, from the Serena CLI and backend configuration to the JetBrains plugin, MCP client, and tool calls, reporting the exact failing boundary. - Use Case: When asked to rename a core service class across a large codebase, the agent confirms the JetBrains semantic backend is live, finds all references, applies a reference-aware rename, checks diagnostics, and reviews the diff. ## Quick Start Use Serena with the JetBrains backend to find all references to this function and rename it safely across the project.
