owl-listener/ai-design-skills

1

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

owl-listener/ai-design-skills

397

2

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

owl-listener/ai-design-skills

345

3

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

owl-listener/ai-design-skills

331

4

generative-ui Designing interfaces where AI generates UI components dynamically.

owl-listener/ai-design-skills

301

5

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

owl-listener/ai-design-skills

294

6

persona-architecture Defining AI character, voice, and personality traits.

owl-listener/ai-design-skills

283

7

progressive-disclosure Revealing AI capability gradually to match user mental models.

owl-listener/ai-design-skills

282

8

context-window-design Designing around token limits, memory, and conversation persistence.

owl-listener/ai-design-skills

280

9

trust-calibration Helping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and s…

owl-listener/ai-design-skills

279

10

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

owl-listener/ai-design-skills

276

11

tone-calibration Adjusting formality, warmth, confidence, and style per context.

owl-listener/ai-design-skills

274

12

chain-of-thought-design Designing reasoning chains that produce better outputs.

owl-listener/ai-design-skills

273

13

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

owl-listener/ai-design-skills

271

14

template-design Creating reusable, parameterised prompt templates for consistent outputs.

owl-listener/ai-design-skills

270

15

frustration-detection Reading user emotional state from text signals — caps, punctuation density, repetition, latency — and adapting before…

owl-listener/ai-design-skills

265

16

few-shot-patterns Crafting examples that steer AI behavior effectively.

owl-listener/ai-design-skills

264

17

emotional-design How the AI responds to user frustration, confusion, delight, and distress.

owl-listener/ai-design-skills

262

18

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

owl-listener/ai-design-skills

259

19

mixed-initiative-flow When the AI leads vs. when the user leads, and how to hand off control.

owl-listener/ai-design-skills

252

20

output-quality-rubrics Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.

owl-listener/ai-design-skills

125

21

task-decomposition Breaking complex user goals into subtasks that agents can handle.

owl-listener/ai-design-skills

123

22

handoff-protocols Designing smooth transitions between agents and between AI and humans.

owl-listener/ai-design-skills

117

23

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

owl-listener/ai-design-skills

114

24

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

owl-listener/ai-design-skills

111

25

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

owl-listener/ai-design-skills

109

26

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

owl-listener/ai-design-skills

109

27

harm-anticipation Proactively identifying failure modes, misuse, and unintended consequences.

owl-listener/ai-design-skills

109

28

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

owl-listener/ai-design-skills

108

29

human-in-the-loop Designing intervention points where humans review, approve, or redirect agent work.

owl-listener/ai-design-skills

108

30

task-success-metrics Measuring whether the AI actually helped users accomplish their goals.

owl-listener/ai-design-skills

108

31

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

owl-listener/ai-design-skills

107

32

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

owl-listener/ai-design-skills

107

33

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

owl-listener/ai-design-skills

106

34

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

owl-listener/ai-design-skills

106

35

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

owl-listener/ai-design-skills

106

36

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

owl-listener/ai-design-skills

106

37

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

owl-listener/ai-design-skills

105

38

domain-voice Tailoring AI behavior for specific professional domains.

owl-listener/ai-design-skills

105

39

error-personality How the AI communicates mistakes, uncertainty, and limitations gracefully.

owl-listener/ai-design-skills

105

40

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

owl-listener/ai-design-skills

105

41

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

owl-listener/ai-design-skills

105

42

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

owl-listener/ai-design-skills

105

43

user-satisfaction-signals Interpreting implicit and explicit feedback — edits, regenerations, abandonment.

owl-listener/ai-design-skills

105

44

value-specification Translating organisational values and user expectations into system constraints.

owl-listener/ai-design-skills

105