Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Owl-Listener/ai-design-skills --skill conversation-patternsgit clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/owl-listener/ai-design-skills/conversation-patterns)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/conversation-patterns"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/conversation-patterns/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/conversation-patterns"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/conversation-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.00499 |
| Opus 5 | $0.00010 | $0.00249 |
| Sonnet 5 | $0.00004 | $0.00100 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
conversation-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 38 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 38 lines · 21 tokens per session scan A c7902f9ff599
conversation-patterns is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 499 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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