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ai-interaction-patterns

Provides 60+ AI interaction design patterns covering prompting, trust, memory, and human-in-the-loop controls.

Updated Apr 2, 2026
Search Tags:#design patterns#interaction design#human-in-the-loop#ai ux#ai trust#ai transparency#prompt ux
One-click install
npx skills add https://github.com/Hamza-Haadi/disease-risk-classifier-hamza --skill ai-interaction-patterns-hamza-haadi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-interaction-patterns
Source: https://github.com/Hamza-Haadi/disease-risk-classifier-hamza/tree/main/.claude/skills/25-ai-interaction-patterns
Command: npx skills add https://github.com/Hamza-Haadi/disease-risk-classifier-hamza --skill ai-interaction-patterns-hamza-haadi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Designing AI-powered interfaces raises questions that general UI guidelines do not answer: how users write prompts, how AI output earns trust, how memory and consent are handled, and how humans stay in control of agentic workflows. This Skill supplies a structured catalog of AI-specific interaction patterns so designers and developers can make consistent, evidence-based decisions. ## Core Features & Use Cases - 60+ patterns in six categories: Wayfinders, Prompt Actions, Tuners, Governors, Trust Builders, and Identifiers, each with design guidance and usage criteria. - Selection frameworks: Decision tables mapping user problems and product types (chat assistant, code copilot, AI agent) to the right patterns, plus a trust-level decision tree. - Anti-pattern guardrails: Critical gotchas such as avoiding photorealistic avatars, never hiding memory contents, and always providing stop controls during generation. - Use Case: When building an enterprise document AI, consult the catalog to combine Attachments, Citations, Caveat, and Disclosure patterns so users can verify AI-generated summaries against sources. ## Quick Start Ask the AI to recommend interaction patterns for a chat assistant that needs onboarding suggestions, follow-up prompts, and transparent memory controls.

Frequently Asked Questions about ai-interaction-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I choose AI interaction patterns for a chat assistant?▼

Start with Open Input, Suggestions, Follow-ups, Memory, and Disclosure as essential patterns for chat assistants. Add Gallery, Voice and Tone, or Branches as nice-to-haves depending on your product's complexity and personalization needs.

What UX patterns build trust in AI-generated content?▼

Use Citations to connect claims to sources, Disclosure to label AI content, Caveat to warn about limitations, and Verification before irreversible actions. Never rely on caveats alone, since users develop caveat blindness over time.

How should AI memory UX be designed for transparency?▼

Memory must never be a black box: show users when memories are saved, and allow viewing, editing, and deletion. Offer scoped or ephemeral memory options and an incognito mode for non-persistent sessions.

When should I use Action Plan and Stream of Thought patterns?▼

Use Action Plan before complex or expensive tasks so users can confirm steps, and show Stream of Thought for any task taking more than 5 seconds. Both patterns keep users informed and prevent abandonment during long generations.

What are common AI UX anti-patterns to avoid?▼

Avoid photorealistic avatars that overpromise capability, overwriting user work without verification, hidden memory storage, and relying solely on caveats for safety. Always provide stop controls during generation and distinguish AI content from human content.

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