Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Owl-Listener/ai-design-skills --skill progressive-disclosuregit clone --depth 1 https://github.com/Owl-Listener/ai-design-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/owl-listener/ai-design-skills/progressive-disclosure)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/progressive-disclosure"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/progressive-disclosure/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/progressive-disclosure"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/progressive-disclosure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.00414 |
| Opus 5 | $0.00009 | $0.00207 |
| Sonnet 5 | $0.00003 | $0.00083 |
| Haiku 4.5 | $0.00002 | $0.00041 |
Grade A, and why
progressive-disclosure scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
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:
- Surface layer: The most obvious, lowest-risk capabilities. Users discover these immediately.
- Intermediate layer: More powerful features revealed through tooltips, suggestions, or first-use prompts.
- 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)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 34 lines · 17 tokens per session scan A d1731fba1770
progressive-disclosure is a skill published in the GitHub repository Owl-Listener/ai-design-skills (172 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 414 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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