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progressive-disclosure

progressive-disclosure

github.com/owl-listenerDesign & UI191321 installsUpdated 20w ago
Repository

About progressive-disclosure

A design skill for revealing AI capabilities gradually, helping product teams avoid overwhelming users while making useful features discoverable. It covers disclosure strategies, capability layers, pacing, and first-use design.

Key features

  1. 01Contextual capability hints
  2. 02Progressive examples
  3. 03Graduated feature access
  4. 04Capability boundary guidance
  5. 05Disclosure maps and flows

Use cases

  1. 01Planning first-use capability guidance
  2. 02Revealing advanced features over time
  3. 03Clarifying what an AI cannot do

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/owl-listener/ai-design-skills --skill progressive-disclosure

After installing, type/progressive-disclosurein Claude Code to confirm it loaded.

Details

From the project README

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)

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