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Prototype

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devton
prototype

Rapid proof-of-concept (POC) prototyping and disposable spike implementation to validate UI/logic hypotheses before specification.

Overview

Publisherdevton
Repositoryagentic-workflow-blueprint
Skill nameprototype
Stars
73
Forks
4
Bundled files
Instructions only
Links
Skill sourceRepository
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by devton on GitHub. Read the source before you install it.

Installation

Install the 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/devton/agentic-workflow-blueprint/tree/main/workflows/mattpocock/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/devton/agentic-workflow-blueprint.git /tmp/agentic-workflow-blueprint
mkdir -p .claude/skills
cp -r /tmp/agentic-workflow-blueprint/workflows/mattpocock/prototype .claude/skills/prototype
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

agentic-workflows-blueprint.workflow.mattpocock.prototype

Goal

Build rapid, lightweight proof-of-concept (POC) prototypes to validate complex user interface or architectural hypotheses and gather empirical feedback prior to formal specification.

Scope

  • Applies to: High-risk UI interactions, complex algorithmic logic, innovative features, dynamic workflows, or untried system flows.
  • Does not cover: Final production-ready implementations or comprehensive test suite writing (use Phase 9 EXECUTE and Phase 10 TESTS).

Triggers

  • "/prototype"
  • "Build rapid prototype spike"
  • "Validate POC hypothesis"
  • "Create prototype implementation"

Inputs

  • hypothesisToValidate: Target feature hypothesis, UI interaction, or algorithm to test.
  • domainModel: Domain entity model specification.
  • researchReport: Technical research findings from Phase 4.
  • uiInvolved: Boolean flag indicating if frontend/UI components are involved.

Invariants (Guardrails)

  1. Time-Bounded Disposable Implementation: Prototypes must focus strictly on proving core hypotheses; refrain from premature polish or production refactoring.
  2. Non-Destructive Feature Branching: Build prototypes in temporary scratch files or isolated feature branches.
  3. Empirical UX/Logic Validation: Run smoke tests or visual generation (e.g., using generate_image or dev server preview) to verify behavior.
  4. Prototype Retrospective: Document key learnings, technical debt to avoid, and refined requirements for /to-spec.

Procedure

1) Define Hypothesis & Validation Criteria
  1. Clearly articulate the specific hypothesis or UX interaction being validated.
  2. Establish explicit pass/fail criteria for the prototype (e.g., response time under X ms, intuitive layout flow, zero layout shifts).
2) Build Lightweight POC
  1. Implement minimal viable logic or UI mock using simplified state or mock data providers.
  2. If UI is involved, apply rapid styling (e.g. CSS flexbox/grid, glassmorphism, or modern web guidance patterns).
3) Execute & Test Prototype
  1. Launch local dev server (npm run dev or equivalent) or execute test script to verify real-time execution.
  2. If UI components are created, generate visual screenshots or inspect rendered DOM elements to validate visual hierarchy.
  3. Conduct interactive smoke testing to verify core user interactions and edge cases.
4) Evaluate Results & Solicit Feedback
  1. Review prototype performance, usability, responsive layout behavior, and failure modes.
  2. Record user or reviewer feedback regarding prototype ergonomics.
5) Compile Prototype Learnings
  1. Document findings in prototype-report.md, summarizing:
    • Hypotheses confirmed vs. refuted.
    • Refined requirements and edge cases discovered.
    • Design patterns or code structures to carry forward into /to-spec.

Outputs

  • prototype-report.md: Prototype retrospective report detailing validated hypotheses, UI/UX feedback, and refined specification requirements.
  • Lightweight disposable code spike.

Review gate

  • Core hypothesis tested with empirical runtime or visual proof.
  • Prototype kept isolated from production main branch.
  • Retrospective learnings recorded in prototype-report.md to feed /to-spec.

References

  • ../../../SKILL.md
  • ../research/SKILL.md
  • ../to-spec/SKILL.md
  • ../../embed-aihero-radioactive/SKILL.md

Frequently asked questions

What does the Prototype AI skill do?

Rapid proof-of-concept (POC) prototyping and disposable spike implementation to validate UI/logic hypotheses before specification.

Why use Prototype on TypingMind?

Because you install it once and use it with any model. 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 Prototype in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/devton/agentic-workflow-blueprint/tree/main/workflows/mattpocock/prototype. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 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 Prototype?

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

Is the Prototype AI skill free?

It is published on GitHub by devton. Check the repository for licensing terms. 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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