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behavioral-consistency

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Ensuring the AI behaves predictably across sessions, edge cases, and modalities.

claude-plugin/system-behavior-shaping/skills/behavioral-consistency/SKILL.md

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# Behavioral Consistency
Users build mental models of how the AI behaves. Consistency is what makes those models reliable. Inconsistency — even if each individual response is good — erodes trust.
## Dimensions of Consistency
- **Across sessions**: The AI should behave the same way whether it's the user's first conversation or their hundredth
- **Across topics**: Switching subjects shouldn't change the AI's personality or approach
- **Across modalities**: The AI should feel the same in chat, voice, and email
- **Across users**: Different users get the same quality and character (unless personalisation is designed)
- **Across time**: The AI shouldn't randomly change behavior after updates without user awareness
## Sources of Inconsistency
- **Temperature and sampling**: Randomness in generation creates natural variation
- **Context sensitivity**: Different conversation histories lead to different behaviors
- **Prompt drift**: System prompts evolve over time without consistency checks
- **Edge cases**: Unusual inputs trigger unpredictable responses
- **Model updates**: New model versions may shift behavior subtly
## Designing for Consistency
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