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Om Mockup Prototype

Organization
open-mercato
om-mockup-prototype

Build a neutral clickable low-fi prototype from a product brief during discovery, after the first synthetic panel and before refreshing the brief or drafting a backlog. Preserve business rules and explicit assumptions, simulate the chosen flow locally, and verify navigation and relevant states in a browser. Use for discovery wireframes or testing a flow before detailed design.

Overview

Publisheropen-mercato
Repositoryskills
Skill nameom-mockup-prototype
Stars
250
Forks
36
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by open-mercato on GitHub. Read the source before you install it.

Installation

Install the Om Mockup Prototype AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/open-mercato/skills/tree/main/skills/om-mockup-prototype
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/open-mercato/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/om-mockup-prototype .claude/skills/om-mockup-prototype
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Om Mockup Prototype in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Om Mockup Prototype on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Om Mockup Prototype is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Discovery prototype

Turn a selected flow from the product brief into neutral screens a person can operate. The result is self-contained local HTML with simulated data, a context record and browser evidence. It tests the sequence, labels, choices and recovery paths before detailed visual design. It works with or without a design system.

In the discovery hand-off, this step follows the first om-synthetic-users panel. The user may then walk the prototype with another panel before om-discover --refresh incorporates the observations and checks readiness for om-backlog. A direct invocation can use a brief without a panel report.

Arguments

  • {brief} (optional): a repository-relative product-brief path. Default ${SPECS_DIR}/product-brief.md.
  • --flow <name-or-id> (optional): the Key flow to simulate. Use the flow already chosen in the current conversation; otherwise propose one from the brief and confirm it.
  • --panel-report <path> (optional): a repository-relative report from the first synthetic panel. With none supplied, use the report returned by the current discovery hand-off; otherwise proceed without one and say so.
  • --slug <slug> (optional): a lowercase kebab-case output name, proposed from the flow without a setup question.
  • --refresh <revision-dir> (optional): revise a previous output of this skill by writing the next new revision. Keep its slug and link the previous revision; never replace existing files.

Workflow

ALWAYS check first: Apply .ai/skills/om-mockup-prototype/SKILL.md when present; safety rules still win.

  1. Load context and safety rules. Follow references/agentic-setup.md and read references/rules.md on every run. Resolve optional config and safe local paths, apply the repository's instructions and permitted override, and load a configured browser descriptor when available. Missing config never starts delivery setup.

  2. Select the flow and preserve its constraints. Read the brief's Key flows, Product, Scope, Business rules, Non-goals, Decisions, Domain glossary and relevant open questions. Read the optional panel report as synthetic hypotheses. Record source paths, relevant stable rule/decision IDs and their status, and what remains unknown. If the brief or a usable flow is missing, stop with the precise missing input. For --refresh, inspect the earlier context and ownership record before drafting a new revision (references/prototype-format.md).

  3. Confirm the simulation boundary. Show the actor, starting point, success outcome, screen/state list, preserved rules and any assumptions the interaction would require. Ask only for information unavailable in the context and for unresolved product choices. Existing explicit authorization for that exact scope stands. A rule conflict needs the human's decision through the brief's required change path; do not resolve it in prototype code. Confirmation approves a simulation and its stated assumptions, not the truth of those assumptions.

  4. Build the clickable revision. Follow references/prototype-format.md: create a fresh revision directory, write the context record, and construct static HTML, CSS and small local event handlers. Use neutral wireframes, accessible controls and simulated data. Wire the selected journey, relevant edge states, back navigation and reset; represent excluded destinations honestly. Record assumptions in the context file without putting panel conclusions or expected reactions into the simulated interface.

  5. Verify the artifact. Run references/quality-gate.md: check scope, offline operation and required interactions, then exercise the local HTML through the browser provider's named operations. Capture actual evidence, close the session, fix failed interactions within the new revision, and recheck affected states. An unresolved static failure means Verification: incomplete, even when it prevents opening the browser. When static review passes but browser access is unavailable, record Verification: not-run; do not claim a browser walk. Failed or partial browser checks also mean incomplete.

  6. Record and report the result. Finalize prototype.json with the file inventory, context sources, previous revision and verification result. Report through references/report-templates.md, with the artifact, limitations and exact Output contract lines. Return to the calling discovery hand-off when present; this skill does not invoke a panel, refresh the brief, file a backlog or start implementation. A direct user may choose a later exact invocation, recorded in Next: without executing it.

Output contract

End with these exact, undecorated lines when an artifact was written:

text
Prototype: <repo-relative path to revision/index.html>
Prototype context: <repo-relative path to revision/README.md>
Verification: passed | incomplete | not-run
Next: none | om-<skill> <exact user-approved arguments>

Consumers parse ^Prototype: (\S+)$, ^Prototype context: (\S+)$, ^Verification: (passed|incomplete|not-run)$ and ^Next: (none|om-[a-z-]+.*)$. Emit only one final Next: line. It names an explicitly chosen, still-unexecuted action; otherwise emit Next: none. Do not emit artifact paths when no files were written. A declined follow-up or completed operation belongs in the prose, not in Next:.

Rules

  • Interactive only. A missing human decision is a stop; no autonomous mode or implicit choice of business policy.
  • Use the existing UI language and the brief's terminology. Neutral styling does not remove accessibility, meaning or useful recovery text.
  • .uxproof/ is read-only context. This skill creates no brand, moodboard, tokens, design contract or production component, and does not require a design system to be extracted first.
  • All displayed records are fictitious simulation data. Never reuse personal interview details, credentials or production records.
  • Keep [SYNTHETIC] and [ASSUMPTION] labels in the context record. Preserve confirmed decisions with their source and owner. Browser checks verify prototype behavior only.
  • Preserve old revisions and manual files. A refresh writes a fresh revision after validating the prior ownership record and its path.
  • Perform no tracker mutations, publishing, dependency installation or application boot. Invoke no unavailable companion skill; report the missing capability instead.
  • Read references/rules.md for the shared writing rules, output markers and evidence distinctions; they always apply.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Om Mockup Prototype AI skill do?

Build a neutral clickable low-fi prototype from a product brief during discovery, after the first synthetic panel and before refreshing the brief or drafting a backlog. Preserve business rules and explicit assumptions, simulate the chosen flow locally, and verify navigation and relevant states in a browser. Use for discovery wireframes or testing a flow before detailed design.

Why use Om Mockup Prototype on TypingMind?

Because you install it once and use it with any model. Om Mockup Prototype is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Om Mockup Prototype in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/open-mercato/skills/tree/main/skills/om-mockup-prototype. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Om Mockup Prototype?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Om Mockup Prototype?

As many as you like. As long as a model supports skills, you can use Om Mockup Prototype with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Om Mockup Prototype AI skill free?

Yes. It is published on GitHub by open-mercato under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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