behavioral-consistency

v2026.09.25

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

GitHub
安装命令
npx skhub add owl-listener/behavioral-consistency
Markdown
SKILL.md

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

  • Behavioral specifications: Document expected behavior for common and edge-case scenarios
  • Golden responses: Maintain a library of reference responses that define the standard
  • Regression testing: When anything changes, test against the golden response library
  • Consistency metrics: Track behavioral variance across sessions and users
  • User expectations: Set and maintain expectations about what the AI does and how

Consistency vs. Adaptation

Consistency doesn't mean rigidity. The AI should adapt to:

  • User preferences (if designed for personalisation)
  • Contextual needs (tone shifts as discussed in tone-calibration)
  • Learning from feedback (if memory systems exist) The key is that adaptation should be predictable and explainable, not random.

Design Artefacts

  • Behavioral specification documents
  • Golden response libraries
  • Regression test suites
  • Consistency monitoring dashboards
  • Adaptation rules (what changes and what stays constant)
发现
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最新版本元数据

版本

v2026.09.25

发布时间

2026年9月25日

分类

未分类

许可证

MIT

源路径

claude-plugin/system-behavior-shaping/skills/behavioral-consistency

默认分支

main

最新提交

f41b650

Tree SHA

e9c55ee