
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)
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| Installs | 244 |
|---|---|
| repo stars | ★ 153 |
| Last updated | June 9, 2026 |
| Repository | owl-listener/ai-design-skills ↗ |
What it does
Helps with ai & agent building tasks during AI-assisted development.
Files
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