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Lean Ux Canvas

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deanpeters·vlatest·frontend

Visualize user journeys, identify pain points, and brainstorm solutions using a dynamically generated Lean UX Canvas.

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add deanpeters/product-manager-skills --skill lean-ux-canvas -a claude-code
  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

deanpeters

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

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  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

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  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned deanpeters/product-manager-skills at cfcb697f14.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Scanned
3 files, 690 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The lean-ux-canvas skill provides a structured framework for product managers to define user problems, desired outcomes, and solution hypotheses. It guides agents through the core components of a Lean UX Canvas to validate ideas before full-scale development.

When to use it

  • Brainstorming initial product concepts with an AI agent to ensure alignment on user needs.
  • Validating assumptions about target audiences and value propositions during early-stage planning.
  • Structuring feedback loops to iterate on features based on real-world user testing results.
  • Creating a shared visual roadmap for cross-functional teams to understand the "why" behind a feature.

Key capabilities

  • Problem Definition: Clearly articulating the specific user pain points being addressed.
  • Desired Outcome: Defining measurable success metrics and goals for the solution.
  • Solution Hypothesis: Drafting potential features or workflows to solve the identified problem.
  • User Persona Mapping: Identifying who the primary users are and their specific contexts.

Example prompts

  • "Help me fill out a Lean UX Canvas for a new mobile fitness tracking app targeting busy professionals."
  • "Critique my current solution hypothesis against the user problems I've defined in this canvas."
  • "Generate three alternative desired outcomes based on the user personas we just discussed."

Tips & gotchas

Ensure you provide specific context about your target audience before asking the agent to generate hypotheses, as generic inputs lead to vague solutions. This skill is best used iteratively; treat the output as a living document that evolves with new data rather than a final specification.