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Conversation Patterns

  • 244 installs
  • 153 repo stars
  • Updated June 9, 2026
  • owl-listener/ai-design-skills

Helps with ai & agent building tasks during AI-assisted development.

About

conversation-patterns is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • conversation-patterns
  • AI & Agent Building
  • AI-coding skill

Conversation Patterns by the numbers

  • 244 all-time installs (skills.sh)
  • +21 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #2,603 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/owl-listener/ai-design-skills --skill conversation-patterns

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Listed on Skillselion
Installs244
repo stars153
Last updatedJune 9, 2026
Repositoryowl-listener/ai-design-skills

What it does

Helps with ai & agent building tasks during AI-assisted development.

Files

SKILL.mdMarkdownGitHub ↗

Conversation Patterns

Conversation between humans and AI follows predictable structural patterns. Designing these deliberately — rather than leaving them to model defaults — is core interaction design work.

Turn-Taking Structure

Every human-AI conversation has a rhythm. The designer decides:

  • Turn length: Short exchanges (chatbot-style) vs. long-form (essay generation). Match turn length to task complexity.
  • Turn initiation: Who speaks first? Does the AI greet, or wait? Does it ask a clarifying question before acting?
  • Turn boundaries: How does the user signal "I'm done"? How does the AI signal "I need more"?

Repair Sequences

Conversations break down. Repair is how they recover:

  • Self-repair: The AI detects its own error and corrects ("Actually, let me revise that...")
  • Other-repair: The user corrects the AI ("No, I meant the other one")
  • Clarification requests: The AI asks for disambiguation before proceeding
  • Graceful misunderstanding: The AI acknowledges confusion without frustrating the user

Design repair sequences explicitly. Don't rely on the model to improvise them.

Grounding

Grounding is how participants establish shared understanding:

  • Confirmation: "Just to confirm, you want me to..."
  • Summarisation: "So far we've covered X, Y, and Z"
  • Reference resolution: Handling pronouns, anaphora, and ambiguous references
  • Context anchoring: Reminding the user what the AI knows and doesn't know

Dialogue Structure Patterns

Common structural patterns for human-AI conversation:

  • Interview: AI asks questions, user answers, AI synthesises
  • Co-creation: Turn-by-turn collaborative building
  • Instruction-execution: User gives command, AI performs, user evaluates
  • Exploration: Open-ended back-and-forth to discover possibilities
  • Guided workflow: AI leads the user through a multi-step process

Choose the pattern that matches the task. Don't default to instruction-execution for everything.

Design Artefacts

  • Conversation flow diagrams showing turn sequences
  • Repair protocol specifications
  • Grounding checkpoints mapped to conversation stages
  • Turn-taking rules per interaction context

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