Skill
bias-detection-design
Designing review workflows to surface and mitigate bias in AI outputs.
@owl-listenerInstall SkillDesign
Skill
/progressive-disclosure
Revealing AI capability gradually to match user mental models.
Install
$ npx -y skills add owl-listener/ai-design-skills --skill progressive-disclosure --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/progressive-disclosure
Context preview
The summary Claude sees to decide when to auto-load this skill.
Revealing AI capability gradually to match user mental models.
SKILL.md
progressive-disclosure.SKILL.mdname: progressive-disclosure
description: Revealing AI capability gradually to match user mental models.
Progressive Disclosure
Users don't understand what AI can do. Progressive disclosure is how you reveal capabilities at the right pace — preventing both overwhelm and underuse.
The Mental Model Gap
Users arrive with mental models shaped by previous technology. They may:
- Treat the AI like a search engine (keyword queries)
- Treat it like a form (expecting rigid structure)
- Underestimate what it can do (asking for less than it offers)
- Overestimate what it can do (expecting perfection)
Progressive disclosure bridges the gap between what users think the AI does and what it actually does.
Disclosure Strategies
- **On-demand hints**: Show capability suggestions contextually ("Did you know you can also ask me to...")
- **Escalating examples**: Start with simple use cases, reveal complex ones as the user gains confidence
- **Feature graduation**: Unlock advanced features after the user demonstrates comfort with basics
- **Contextual teaching**: When the user attempts something inefficiently, show a better approach
- **Capability boundaries**: Clearly communicate what the AI cannot do, not just what it can
Layered Capability Revelation
Structure capabilities in layers: 1. **Surface layer**: The most obvious, lowest-risk capabilities. Users discover these immediately. 2. **Intermediate layer**: More powerful features revealed through tooltips, suggestions, or first-use prompts. 3. **Power layer**: Advanced capabilities for experienced users — available but not promoted.
Pacing
- **Too fast**: Users feel overwhelmed, ignore capabilities, or lose trust
- **Too slow**: Users get bored, think the product is limited, churn
- **Just right**: Each new capability feels like a natural next step
Design Artefacts
- Capability disclosure maps showing what's revealed when
- Mental model progression diagrams
- First-use experience flows with disclosure triggers
- Capability tier definitions (surface, intermediate, power)
Read more
name: progressive-disclosure description: Revealing AI capability gradually to match user mental models.
Progressive Disclosure
Users don't understand what AI can do. Progressive disclosure is how you reveal capabilities at the right pace — preventing both overwhelm and underuse.
The Mental Model Gap
Users arrive with mental models shaped by previous technology. They may:
- Treat the AI like a search engine (keyword queries)
- Treat it like a form (expecting rigid structure)
- Underestimate what it can do (asking for less than it offers)
- Overestimate what it can do (expecting perfection)
Progressive disclosure bridges the gap between what users think the AI does and what it actually does.
Disclosure Strategies
- **On-demand hints**: Show capability suggestions contextually ("Did you know you can also ask me to...")
- **Escalating examples**: Start with simple use cases, reveal complex ones as the user gains confidence
- **Feature graduation**: Unlock advanced features after the user demonstrates comfort with basics
- **Contextual teaching**: When the user attempts something inefficiently, show a better approach
- **Capability boundaries**: Clearly communicate what the AI cannot do, not just what it can
Layered Capability Revelation
Structure capabilities in layers: 1. **Surface layer**: The most obvious, lowest-risk capabilities. Users discover these immediately. 2. **Intermediate layer**: More powerful features revealed through tooltips, suggestions, or first-use prompts. 3. **Power layer**: Advanced capabilities for experienced users — available but not promoted.
Pacing
- **Too fast**: Users feel overwhelmed, ignore capabilities, or lose trust
- **Too slow**: Users get bored, think the product is limited, churn
- **Just right**: Each new capability feels like a natural next step
Design Artefacts
- Capability disclosure maps showing what's revealed when
- Mental model progression diagrams
- First-use experience flows with disclosure triggers
- Capability tier definitions (surface, intermediate, power)
Ships withai-design-skills
Part of the Designer Skills suite. Install this collection and four more with one command in Claude Code: /plugin marketplace add Owl-Listener/designer-skills This repo still works on its own. The suite just gives everyone one front door.
Get the whole plugin
Other skills on ai-design-skills.
@owl-listenerInstall SkillSkill
consent-and-agency
Designing for informed user consent, opt-out, and human override.
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escalation-design
When and how AI should escalate to humans, refuse, or ask for clarification.
@owl-listenerInstall SkillSkill
guardrail-design
Defining behavioral boundaries — what the AI should and shouldn't do.
@owl-listenerInstall SkillSkill
harm-anticipation
Proactively identifying failure modes, misuse, and unintended consequences.
@owl-listenerInstall SkillSkill
transparency-patterns
Showing users what the AI knows, doesn't know, and how confident it is.
@owl-listenerInstall Skill