Conversation Patterns
Turn-taking, repair sequences, grounding, and dialogue structure for human-AI interaction.
Install
$ agentstack add skill-owl-listener-ai-design-skills-conversation-patterns ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-owl-listener-ai-design-skills-conversation-patterns) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Owl-Listener
- Source: Owl-Listener/ai-design-skills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.