Authentic Product Representation logo

Authentic Product Representation

Organization
dembrandt
authentic-product-representation

Product visuals must show the real product with real data. Use when building landing pages, hero sections, screenshots or demo panels.

Overview

Publisherdembrandt
Repositorydembrandt-skills
Skill nameauthentic-product-representation
Stars
169
Forks
21
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Authentic Product Representation 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/dembrandt/dembrandt-skills/tree/main/skills/authentic-product-representation
  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/dembrandt/dembrandt-skills.git /tmp/dembrandt-skills
mkdir -p .claude/skills
cp -r /tmp/dembrandt-skills/skills/authentic-product-representation .claude/skills/authentic-product-representation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Authentic Product Representation 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 Authentic Product Representation 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 Authentic Product Representation 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.

Authentic Product Representation

The fastest way to lose a visitor's trust is a product visual that is obviously staged. People have seen thousands of landing pages; they recognise a fabricated dashboard, a screenshot full of lorem ipsum, or a "terminal" that prints things the real tool never would. The visual that converts is the one that looks like it was screenshotted straight out of the working product — because it was, or because it was built to be indistinguishable from it.

The principle is simple: the visual is the product, not a poster of it. Every shortcut away from that — invented data, decorative chrome, a capability shown that does not exist — is a small withdrawal from the trust account.

This skill governs whether a visual is credible; what it has to accomplish in the page's argument comes from the product narrative framework in [[layout-paradigms-and-consistency]]. A fabricated proof point proves nothing.


Design With Real Content

Idealised placeholder content hides the problems your design will actually face.

  • No lorem ipsum. Use real copy, or the closest draft of it. Real words have real lengths, real line-wraps, real awkward edge cases. Latin filler is uniformly tidy in a way your content never will be.
  • Real names, real numbers, real lengths. "John Smith / $1,234" is a fantasy dataset. Use the longest realistic name, the account with 0 items, the title that wraps to three lines. If the design only looks good with perfect data, it is not finished.
  • Design the empty, loading, error, and overflow states too — they are the product as much as the happy path. A screenshot that only ever shows the full, perfect state is selling a product that does not exist.

If you genuinely cannot use production data, generate the mock from the real schema and the real renderer, so its shape, formatting, and constraints match what ships.


Mirror the Real Output

When a visual stands in for what the product produces — a CLI run, a generated file, a report, a chart, an editor — reproduce the actual format, not a prettier reinterpretation.

  • Use the product's real output structure: the same labels, the same ordering, the same tree/log/table shape, the same truncation rules ("+4 more").
  • Pull the values from a real run where possible. If the visual claims to process yourdomain.com, show what processing yourdomain.com actually yields — not another site's values relabelled.
  • Reuse the real rendering component if one exists. A faithful embed beats a hand-built lookalike that drifts from reality the moment the product changes.
  • Breadth is more convincing shown than claimed. Listing the real categories the tool already covers ("also: borders, shadows, motion, components") communicates scope truthfully; a "coming soon" teaser for things that already exist communicates the opposite.

A reinterpreted mock is a maintenance liability and a credibility risk: it looks like the product until someone compares it to the product.


Keep It Truthful

The visual makes claims. Every one must hold.

  • Numbers reflect reality. Stats, counts, and metrics are real or clearly illustrative — never inflated fiction presented as fact.
  • Labels match content. Do not relabel one thing as another (a screenshot of system A captioned as system B; a domain in the command that does not match the results shown).
  • Show only real capabilities. A demo that performs a feature the product does not have is a promise you will break on first use.
  • "Soon" means soon, and rarely earns its place. A pending/soon badge on something that already ships is a lie; a pending badge on a real roadmap item is usually just noise in a product shot. Prefer showing what works.

Refuse Fabricated Marketing Chrome

A recognisable visual dialect signals "generated to look impressive" rather than "screenshotted from a real product." It reads as AI-slop and erodes the authenticity you are trying to build.

Avoid:

  • Invented dashboards, fake charts, and decorative "terminals" that print marketing copy
  • Gradient-filled text, sparkle/star accents, aurora glows, and glassmorphism used as a substitute for substance
  • Typewriter/rotator headlines and faux-AI chrome (scanning lines, "thinking" shimmers) with no real function
  • Pill badges and ornaments that exist only to fill space, especially in places where they clash with the medium (e.g. a label chip dropped into monospace output)

The restraint test: if an element does not exist in the real product, it does not belong in a visual that represents the real product. Decoration that carries no information is the first thing to cut.


When You Must Stage

Sometimes a literal screenshot is impossible (pre-launch, sensitive data, a composite view). Stage honestly:

  • Build from the real schema, the real component, and realistic values.
  • Match the product's real states and formatting, including the unglamorous ones.
  • Keep claims conservative — under-promise in the visual, let the product over-deliver.
  • Label clearly illustrative figures as such rather than passing them off as measured.

Photos on a Tinted Stage

A product shot on a tinted tile needs a transparent cutout. An opaque shot with a baked-in white background shows as a white box on the tint. Where cutouts do not exist, multiply-blend the photo onto the tile so the white vanishes.

When a design export and its own screenshot disagree, the screenshot wins. The export is the tool's opinion of the file. The rendering is what shipped.


Review Checklist

  • Is there any lorem ipsum or obviously idealised placeholder content left? (There should be none.)
  • Does the content use realistic lengths — the long name, the empty list, the three-line title?
  • Are the empty, loading, error, and overflow states represented, not just the perfect state?
  • When the visual stands in for product output, does it match the real format, ordering, and truncation?
  • Do the values correspond to the input shown (right domain, right source), not another case relabelled?
  • Is the real rendering component reused where one exists, instead of a hand-built lookalike?
  • Do product photos on tinted surfaces use transparent cutouts or a multiply blend?
  • Are all numbers, labels, and capabilities truthful — nothing inflated, mislabelled, or not-yet-real?
  • Is there any "coming soon" framing on things that already ship?
  • Is there fabricated marketing chrome (fake dashboards, gradient text, sparkles, decorative terminals) that should be cut?
  • Could a visitor screenshot the real product and get the same impression this visual gives? If not, why not?

Frequently asked questions

What does the Authentic Product Representation AI skill do?

Product visuals must show the real product with real data. Use when building landing pages, hero sections, screenshots or demo panels.

Why use Authentic Product Representation on TypingMind?

Because you install it once and use it with any model. Authentic Product Representation 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 Authentic Product Representation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dembrandt/dembrandt-skills/tree/main/skills/authentic-product-representation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Authentic Product Representation?

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 Authentic Product Representation?

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

Is the Authentic Product Representation AI skill free?

Yes. It is published on GitHub by dembrandt 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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