Gene transfusion is how we move a working pattern from one codebase into another.
In its basic form, it consists of directing coding agents to concrete exemplars drawn from inside or outside the StrongDM code estate.
Example
Identifying Caddy's Let's Encrypt integration as a reference and using it as the basis for synthesizing native Let's Encrypt support in another module.
Pattern Propagation
Once introduced, patterns spread through a code estate. When we pair a solution with a validation-friendly exemplar, we can reproduce it in other contexts. We use this to:
Cross-Language Application
Reuse patterns across languages (for example, Go to Python or TypeScript)
Direct Inlining
Be inlined directly into an existing system without abstraction overhead
Library Embodiment
Be embodied as a conventional library or dependency relationship
"Gene transfusion functions as a mechanism for structure reuse without shared authorship or manual refactoring.
The Flow
01
Identify Exemplar
Find working implementation (internal or external)
02
Extract Pattern
Agent analyzes structure, invariants, edge cases
03
Synthesize
Generate equivalent implementation in target context
04
Validate
Behavioral tests confirm equivalence
05
Propagate
Pattern becomes available for future transfusions
The key insight is that patterns encode solutions to recurring problems. With a working exemplar and good tests, an agent can reproduce the same behavior in a new context while adapting to local constraints.