codebase-intel

Blast radius analysis maps the dependency graph to identify every file affected by a proposed code change. This impact analysis calculates a risk score from 0 to 100 and halts execution for scores above 80 to prevent unsafe modifications.

Dependency graph impact analysis uses precomputed structural data to answer repository questions deterministically rather than guessing. It evaluates code dependencies to provide architecture discovery, dead code detection, and circular dependency audits across multiple languages.

Finding dead code and circular dependencies relies on querying a precomputed structural graph database of your repository. This graph intelligence maps file relationships, enabling automated detection of unused modules and dependency loops without rereading raw source code.

Code intelligence tools trace git history attribution by mapping commit data onto the structural dependency graph. This contextualizes architectural evolution, answering where specific implementations reside and how components interact across the codebase.

Risk scoring requires a precomputed graph database lookup combined with a MASTER.md context file. These inputs provide the deterministic structural facts and architectural context needed to calculate file risk scores and evaluate blast radius accurately.

Structural graph limitations include potential unmapped or low-coverage areas within the codebase that require gap filling. The analysis only adds edges above a confidence threshold and must be refreshed incrementally to maintain accurate dependency mappings.