Lenny Skills is a collection of product-management and engineering workflows for Claude Code and other AI agents, covering areas such as strategy, research, planning, shipping, growth, and hiring. Each skill gives an agent specialized guidance, frameworks, checklists, or templates for product work, and the catalogue contains many of these skills.
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 RefoundAI/lenny-skills --skill ai-native-uxgit clone --depth 1 https://github.com/RefoundAI/lenny-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/refoundai/lenny-skills/ai-native-ux)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/ai-native-ux"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/ai-native-ux/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/refoundai/lenny-skills/ai-native-ux"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/ai-native-ux.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00030 | $0.01602 |
| Opus 5 | $0.00015 | $0.00801 |
| Sonnet 5 | $0.00006 | $0.00320 |
| Haiku 4.5 | $0.00003 | $0.00160 |
Grade A, and why
ai-native-ux 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Designing AI-Native User Experiences
Transition from static interfaces to fluid, intent-driven interactions that leverage model intelligence.
Help the user with designing ai-native user experiences using insights from 14 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Map the Agency-Control Balance - Assist the user in deciding which tasks the AI should automate versus which requires manual human oversight.
- Design for Non-Determinism - Help create UI patterns that account for varying model outputs and provide easy correction mechanisms.
- Structure Conversational Grammars - Guide the user in defining the invisible rules and structured elements that make natural language interfaces predictable.
- Iterate on Feedback Loops - Help implement simple, high-frequency feedback mechanisms to refine model performance based on user interaction.
Core Principles
Design for fluid intent
Aishwarya Naresh Reganti + Kiriti Badam: "Most people tend to ignore the non-determinism. You don't know how the user might behave with your product, and you also don't know how the LLM might respond to that. The second difference is the agency control trade-off."
Move away from fixed buttons and forms toward interfaces where user intentions are expressed through natural language. This requires managing the trade-off between the flexibility of language and the precision of traditional UI.
Define clear decision boundaries
Adriel Frederick: "And I was like yeah, the reason that falls down is the algorithms don't understand long term effects often, nor do they understand how people might respond to it, nor do they understand your intent for the product, and I think it's really important for product managers to play that role. That is our job. When you are working on algorithmic heavy products, your job is figuring out what the algorithm should be responsible for, what people are responsible for, and the framework for making decisions."
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 92 lines · 30 tokens per session scan A 55c9ceb7706b
ai-native-ux is a skill published in the GitHub repository RefoundAI/lenny-skills (1,318 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,602 once invoked, about $0.0002 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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