agentleFS
jiayuewangjavyjiayuewangjavy/product-designer-skills-os/skills/interactive-prototype/SKILL.md

interactive-prototype

Skill4 starsChanged 17 days agoMITView on GitHub

Build or specify a minimal interactive product prototype from a testable prototype question. Use when interaction, state, timing, or behavior must be experienced to gather evidence. Do not use for production implementation or purely visual mockups that require no interaction.

3 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

7 rules. 0 are common, 7 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 3 edited versions we read keep these.

  • 7

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Create the smallest runnable artifact that exposes the target behavior and preserves the product decisions already made.

    line 8
  2. Distinctive:

    Add only the minimum instrumentation or observation hooks needed for the evidence plan.

    line 22
  3. Distinctive:

    Keep the artifact easy to reset and share with intended reviewers or participants.

    line 23
  4. Distinctive:

    Do not silently expand a prototype into production architecture.

    line 31
  5. Distinctive:

    Do not add accounts, payments, destructive actions, live customer data, or external publication without explicit authorization.

    line 32
  6. Distinctive:

    Do not polish unrelated surfaces after the prototype can already answer its question.

    line 33
  7. Distinctive:

    Do not claim usability, desirability, or feasibility without observed evidence.

    line 34

“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Interactive Prototype

Create the smallest runnable artifact that exposes the target behavior and preserves the product decisions already made.

Before building

Require or infer a precise prototype question, audience, required fidelity, critical states, evidence plan, and stop rule. If these are missing and would materially change the artifact, route to prototype-question first.

Inspect available product context, conceptual model, design system, existing code, and prototype tools. Reuse an existing artifact when it can answer the question with a smaller change. Choose an available implementation method; do not require a particular external engine.

Build for evidence

  • Implement only the critical path and states required by the question.
  • Make loading, empty, error, success, permission, and recovery states real when they affect the decision.
  • Label simulated data or behavior; isolate it from production systems.
  • Preserve the user's confirmed terminology, constraints, hierarchy, and interaction intent.
  • Add only the minimum instrumentation or observation hooks needed for the evidence plan.
  • Keep the artifact easy to reset and share with intended reviewers or participants.

Handoff

Return the runnable artifact or implementation path, how to operate it, what is simulated, known limitations, the evidence to collect, and the stop rule. After testing, route observations to learning-loop-readout rather than treating completion as validation.

Boundaries

  • Do not silently expand a prototype into production architecture.
  • Do not add accounts, payments, destructive actions, live customer data, or external publication without explicit authorization.
  • Do not polish unrelated surfaces after the prototype can already answer its question.
  • Do not claim usability, desirability, or feasibility without observed evidence.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.