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behavioral-consistency

behavioral-consistency

github.com/owl-listenerDesign & UI191130 installsUpdated 20w ago
Repository

About behavioral-consistency

A behavioral-consistency skill for AI product teams, focused on defining how an AI should act across sessions, topics, users, and updates. It uses specifications, reference responses, and regression tests while allowing predictable adaptation.

Key features

  1. 01Specify behavior for common and edge cases
  2. 02Maintain a library of reference responses
  3. 03Run regression tests after changes
  4. 04Track behavior variance across sessions and users
  5. 05Define explainable adaptation rules

Use cases

  1. 01Set behavior standards across assistant topics
  2. 02Check for regressions after model or prompt changes
  3. 03Monitor consistency across different users

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/owl-listener/ai-design-skills --skill behavioral-consistency

After installing, type/behavioral-consistencyin Claude Code to confirm it loaded.

Details

From the project README

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)

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