ai-design-skills

Proactively identifying failure modes, misuse, and unintended consequences.

Managing shared context, memory, and state across multiple agents.

Coordinating text, image, voice, and tool-use modalities in a single interaction.

Helping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and source signalling.

Reading user emotional state from text signals — caps, punctuation density, repetition, latency — and adapting before the user disengages.

Designing review workflows to surface and mitigate bias in AI outputs.

Designing for informed user consent, opt-out, and human override.

When and how AI should escalate to humans, refuse, or ask for clarification.

Defining behavioral boundaries — what the AI should and shouldn't do.

Showing users what the AI knows, doesn't know, and how confident it is.

Translating organisational values and user expectations into system constraints.

Defining what each agent does, knows, and owns in a multi-agent system.

What happens when an agent fails — retry, fallback, escalate, or graceful degradation.

Designing smooth transitions between agents and between AI and humans.

Designing intervention points where humans review, approve, or redirect agent work.

Making multi-agent workflows visible and debuggable for designers and developers.

Breaking complex user goals into subtasks that agents can handle.

A/B testing, side-by-side comparison, and preference ranking for AI outputs.

Classifying AI failures — hallucination, refusal, irrelevance, tone mismatch, latency.

Adapting Nielsen's heuristics and new AI-specific heuristics for AI interfaces.

Tracking AI product quality over time — drift, degradation, and improvement.

Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.

Measuring whether the AI actually helped users accomplish their goals.

Interpreting implicit and explicit feedback — edits, regenerations, abandonment.

Designing around token limits, memory, and conversation persistence.

Turn-taking, repair sequences, grounding, and dialogue structure for human-AI interaction.

User correction, thumbs up/down, inline editing, and reinforcement signals.

Designing interfaces where AI generates UI components dynamically.

When the AI leads vs. when the user leads, and how to hand off control.

Revealing AI capability gradually to match user mental models.

Designing reasoning chains that produce better outputs.

Defining output format, length, tone, and content boundaries within prompts.

Designing what information goes into the context window and in what order.

Crafting examples that steer AI behavior effectively.

Managing prompt iterations, testing changes, and tracking what works.

Anatomy of effective system prompts — role, context, constraints, format.

Creating reusable, parameterised prompt templates for consistent outputs.

Ensuring the AI behaves predictably across sessions, edge cases, and modalities.

Adapting AI behavior for different cultural contexts, languages, and norms.

Tailoring AI behavior for specific professional domains.