code-journey

Trace Ori code execution through the compiler pipeline by using the code-journey skill to automate end-to-end analysis. It follows code through lexing, parsing, type checking, interpretation, and LLVM codegen to identify inefficiencies and potential issues.

LLVM IR analysis debugs performance bottlenecks by performing deep scrutiny on generated intermediate representation. It identifies instruction inefficiencies, ARC correctness issues, attribute application, control flow patterns, and binary quality metrics to pinpoint exact problems.

Yes, analyzing control flow and binary metrics requires access to the Ori compiler toolchain and associated analysis scripts. The skill automates scrutiny of generated LLVM IR but depends on the external toolchain to generate the necessary intermediate representation.

The best way to identify ARC correctness issues in generated LLVM IR is to automate analysis through the compiler pipeline. This approach scrutinizes instruction purity, attribute usage, and control flow patterns to detect memory management bottlenecks or correctness issues.

Yes, you can analyze lexer and parser output for type checking inefficiencies in Ori. The skill traces execution from source text through type checking and interpretation, ultimately examining the final machine code generation for potential compiler issues.

Limitations when extracting instruction metrics from LLVM codegen include dependency on the external Ori compiler toolchain. Without the associated analysis scripts and toolchain environment, the skill cannot generate or evaluate the intermediate representation required for binary quality metrics.