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Agent skill

AI Assisted Prototyping

by RefoundAI in RefoundAI/lenny-skills

Help users build functional product prototypes from natural language or visual mocks using AI coding tools.

MITAuto-check passedDevelopment

Install AI Assisted Prototyping

skills CLI
$ npx skills add RefoundAI/lenny-skills --skill ai-assisted-prototyping -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install RefoundAI/lenny-skills ai-assisted-prototyping --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-assisted-prototyping .claude/skills/ai-assisted-prototyping && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-assisted-prototyping
GitHub stars
1.4k
Token cost
~1.2k tokens
SKILL.md length
669 words
Files
3 (incl. references)
Skills in repo
76
Repo updated
First seen
Licence
MIT

At a glance

Help users build functional product prototypes from natural language or visual mocks using AI coding tools.

  • Works in 4 steps: Identify the goal - Determine if the… → Select the tool - Choose between… → Prompt and iterate - Use detailed… → …
  • Tasks that involve Prototyping
  • SKILL.md covers How to Help, Core Principles, Questions to Help Users and Common Mistakes to Flag, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AI Assisted Prototyping is an agent skill from RefoundAI/lenny-skills. Help users build functional product prototypes from natural language or visual mocks using AI coding tools. This skill enables product leaders to bypass engineering bottlenecks and validate ideas through hands-on building.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/artifacts.md` and `references/guest-insights.md`).

It sits in Development, covering Prototyping. The repository describes itself as: 86 product management skills from Lenny's Podcast for Claude Code and AI agents. Hiring, user research, strategy, shipping, and more. The licence is MIT.

When your agent uses it

  • Tasks that involve Prototyping

Example prompts

  • “/ai-assisted-prototyping”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Identify the goal - Determine if the objective is a visual exploration, a functional internal tool, or a production-grade feature…
  2. Select the tool - Choose between web-based visual builders like v0 or Lovable and local development environments like Cursor based on…
  3. Prompt and iterate - Use detailed descriptions, PRDs, or screenshots to generate the initial version and refine it through granular…
  4. Validate and hand off - Use the interactive prototype to gather user feedback or provide engineering with high-fidelity reference code for…

What it can do on your machine

Read from SKILL.md and the folder at commit 13598cc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Assisted Prototyping loads about 1.2k tokens when it runs, and up to ~24k if it reads all its reference files. Until then it costs about 62 tokens; SKILL.md has 669 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~62
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~24k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from RefoundAI/lenny-skills at commit 13598cc, republished under its MIT licence (© RefoundAI). 669 words, ~1,201 tokens.

Download SKILL.mdSave it as .claude/skills/ai-assisted-prototyping/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
ai-assisted-prototyping
description
Help users build functional product prototypes from natural language or visual mocks using AI coding tools. This skill enables product leaders to bypass engineering bottlenecks and validate ideas through hands-on building.

AI-Assisted Prototyping

Transform abstract product concepts into functional, interactive software using natural language and AI tools.

Help the user with ai-assisted prototyping using insights from 15 guests and posts across Lenny's Podcast and Newsletter.

How to Help

  1. Identify the goal - Determine if the objective is a visual exploration, a functional internal tool, or a production-grade feature validation.
  2. Select the tool - Choose between web-based visual builders like v0 or Lovable and local development environments like Cursor based on project complexity.
  3. Prompt and iterate - Use detailed descriptions, PRDs, or screenshots to generate the initial version and refine it through granular, sequential feedback.
  4. Validate and hand off - Use the interactive prototype to gather user feedback or provide engineering with high-fidelity reference code for implementation.

Core Principles

Taste-Making through Functional Builds

Aparna Chennapragada: "If you're not prototyping and building to see what you want to build, I think you're doing it wrong. It becomes even more important to have that territorial and taste-making at the heart of it because, otherwise, you just have a Frankenstein product."

Build functional prototypes immediately to develop product taste. Visualizing the vision before committing to full-scale development helps avoid building incoherent features that fail to solve core user problems.

Real-World Feature Validation

Eric Simons: "And it's not just building a static site, or something like that, but you can actually build full stack, real software with databases, and hosting and et cetera, just from prompting. And in a ridiculously short period of time, it's not like you're spending hours and hours or days, putting this together. You can get results in like, a minute."

Move beyond static mockups to validate complex features like databases and hosting. Text-to-app tools allow for the testing of full-stack versions of features rather than just static UIs.

Planning Over Execution

Lazar Jovanovic: "I can say I spent 80% of my time in planning and chatting and only 20% in executing the plan actually. I'm optimizing for the right kind of speed. Most people optimize for the wrong one."

Successful AI orchestration requires shifting focus from implementation to heavy planning and sequential task scoping. Dedicate the majority of project time to the chatting phase to ensure the AI follows a logical path.

Show full SKILL.md (298 more words)Show less
Independent Shipping

Zevi Arnovitz: "If you're non-technical like me, code is terrifying, but AI just makes it so much possible. In the next coming years, I think everyone's going to become a builder. Titles are going to collapse and responsibilities are going to collapse."

Lower the barrier to entry for professional software development by using tools like Cursor with Claude Code. This allows non-technical product managers to build and ship software independently using a library of reusable commands.

Questions to Help Users

  • "What specific problem or manual workflow are you trying to automate with this prototype?"
  • "Do you have a visual reference, such as a Figma screenshot or a hand-drawn sketch, to use as a starting point?"
  • "Does your prototype require a functional backend for data persistence and user authentication?"
  • "Which AI development tool best fits your current technical skill level and project needs?"
  • "What are the 2-3 core functional requirements that will define the success of this build?"
  • "How do you plan to use the final output: for user testing, internal alignment, or engineering handoff?"

Common Mistakes to Flag

  • Neglecting the planning phase - Jumping into implementation without a sequential task list leads to architectural confusion for the AI and broken logic.
  • Building static instead of functional - Missing the opportunity to test real data flows and hosting environments by settling for UI-only mocks limits validation.
  • Using the wrong tool for the task - Attempting complex state management in a simple visual builder when a professional local IDE is required for advanced debugging.
  • Vague initial prompts - Failing to provide enough context about the desired software stack and core features results in generic and unhelpful outputs.

Deep Dive

For all 40 sourced insights from 15 guests, see references/guest-insights.md

  • Writing Prds
  • Shipping Velocity
  • Building With Ai Agents
  • Product Tool Stack

© RefoundAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/ai-assisted-prototyping of RefoundAI/lenny-skills.

  • SKILL.md
  • references/artifacts.md
  • references/guest-insights.md

Open the folder on GitHubat commit 13598cc

Compare with similar skills

AI Assisted Prototyping next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Assisted Prototyping compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Assisted Prototyping this skillRefoundAI/lenny-skills1.4k—~1.2kAutomated safety check: PassMIT
Native Feel Cross Platform Desktopyetone/native-feel-skill1.9k1 repos~1.5kAutomated safety check: PassMIT
Compound Engineering PrototypeEveryInc/compound-engineering-plugin25k—~1.9kAutomated safety check: PassMIT
Collaborating With CodexGuDaStudio/collaborating-with-codex1321 repos~712Automated safety check: PassMIT
Digikeyaklofas/kicad-happy1.4k1 repos~4.5kAutomated safety check: PassMIT
Collaborating With Geminihaoyu-haoyu/Multi-AI-Workflow109—~521Automated safety check: PassMIT

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Categories

Questions about AI Assisted Prototyping

What does AI Assisted Prototyping do?

Help users build functional product prototypes from natural language or visual mocks using AI coding tools. AI Assisted Prototyping is an agent skill from RefoundAI/lenny-skills. Help users build functional product prototypes from natural language or visual mocks using AI coding tools.

When should I use AI Assisted Prototyping?

AI Assisted Prototyping fits situations like: tasks that involve Prototyping.

How do I install AI Assisted Prototyping in Claude Code?

Run `npx skills add RefoundAI/lenny-skills --skill ai-assisted-prototyping -a claude-code`. Or copy the skill folder (skills/ai-assisted-prototyping in RefoundAI/lenny-skills) into .claude/skills/ai-assisted-prototyping in your project. Claude Code loads it when a task matches its description.

How do I install AI Assisted Prototyping in Codex?

Run `npx skills add RefoundAI/lenny-skills --skill ai-assisted-prototyping -a codex`. Or copy the skill folder (skills/ai-assisted-prototyping in RefoundAI/lenny-skills) into .agents/skills/ai-assisted-prototyping in your project. Codex loads it when a task matches its description.

Can I use AI Assisted Prototyping in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add RefoundAI/lenny-skills --skill ai-assisted-prototyping -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-assisted-prototyping, .gemini/skills/ai-assisted-prototyping, .github/skills/ai-assisted-prototyping and .opencode/skills/ai-assisted-prototyping in your project.

What does AI Assisted Prototyping need to run?

SKILL.md names no scripts, command-line tools or credentials: AI Assisted Prototyping is instructions for the agent only.

Does AI Assisted Prototyping access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI Assisted Prototyping safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Assisted Prototyping use?

AI Assisted Prototyping is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Assisted Prototyping use?

About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 23k tokens, read only when the agent opens those files.

What are the alternatives to AI Assisted Prototyping?

Skills that share tags, products or a category with AI Assisted Prototyping: Native Feel Cross Platform Desktop (yetone/native-feel-skill, 1.9k stars), Compound Engineering Prototype (EveryInc/compound-engineering-plugin, 25k stars), Collaborating With Codex (GuDaStudio/collaborating-with-codex, 132 stars) and Digikey (aklofas/kicad-happy, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Assisted Prototyping?

RefoundAI (a GitHub organization) maintains it in RefoundAI/lenny-skills, which has 1,382 GitHub stars. The repository holds 76 skills in this directory. The repository was last updated on July 16, 2026.

Source: RefoundAI/lenny-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.