csi-persona-conformance

To validate AI persona conformance, run static harness checks and behavioral case scoring against frozen baseline records. This process verifies required floor nouns, instruction length, and refusal patterns to detect any persona drift after edits or rewrites.

Persona drift occurs when an AI agent deviates from its established behavioral contract. You audit it by comparing current persona documents against a frozen baseline, running static checks and behavioral case scoring to isolate intentional voice edits from accidental regressions.

Yes, you can use standard Python 3 library execution to run static harness testing. This checks that persona files adhere to required floor nouns, instruction length, and notation standards without needing external dependencies.

To compare two versions of an AI persona, use A/B voice comparison to isolate and evaluate changes. This distinguishes between intentional voice edits and accidental contract regressions by scoring behavioral prompts against both versions.

Behavioral case scoring limitations include its reliance on running specific prompts against personas to verify refusal patterns and marker responses. It requires a frozen baseline record to compare against, making it dependent on initial contract accuracy.

No, you do not need external dependencies to perform AI persona validation. The skill requires only standard Python 3 library execution to perform static harness checks and behavioral case scoring against frozen baselines.