What problem does it solve? Interpreting raw CPU profiles and heap snapshots is slow and error-prone, and loading them directly into an AI context wastes tokens. This Skill turns an existing N|Solid diagnostic asset into a structured, token-efficient analysis that pinpoints CPU bottlenecks and memory leak patterns. ## Core Features & Use Cases - Asset summarization by ID: Resolves an asset ID (or app name plus asset type) and retrieves a token-optimized summary via the asset-summary MCP tool, with a polling loop for asynchronously summarized heap snapshots. - Type-specific analysis: Identifies hot functions and self/total time costs in CPU profiles, top allocating constructors in heap samples, and retained-size objects, dominator chains, and common leak patterns in heap snapshots. - Runtime correlation: Cross-checks findings with historic metrics and events to distinguish a real leak from a one-off spike, and can fetch runtime code for the hottest user-owned frame. - Use Case: After capturing a heap snapshot of a production Node.js service, ask for an analysis and receive a structured report listing the largest retained objects, what holds references to them, and whether historic heap metrics confirm a growing leak. ## Quick Start Ask the assistant to analyze N|Solid asset with a given asset ID and explain the main bottleneck or leak findings.
