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Gdd Benchmark

Community
hegemonart
gdd-benchmark

Harvest and synthesize per-component design benchmarks from 18 design systems and produce canonical component specs at `reference/components/<name>.md`. Use when adding a new component spec, running a benchmark wave, listing corpus coverage, or refreshing a spec after a design-system version bump.

Overview

Publisherhegemonart
Repositoryget-design-done
Skill namegdd-benchmark
Stars
3
Forks
1
Bundled files
Instructions only
LicenseMIT
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 hegemonart on GitHub. Read the source before you install it.

Installation

Install the Gdd Benchmark 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/hegemonart/get-design-done/tree/main/scripts/skill-templates/benchmark
  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/hegemonart/get-design-done.git /tmp/get-design-done
mkdir -p .claude/skills
cp -r /tmp/get-design-done/scripts/skill-templates/benchmark .claude/skills/gdd-benchmark
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gdd Benchmark 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 Gdd Benchmark 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 Gdd Benchmark 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.

{{command_prefix}}benchmark

Harvest per-component design knowledge from 18 design systems and synthesize canonical specs at reference/components/<name>.md. The 18-source corpus + fallback chain lives in ../../connections/design-corpora.md. Per-skill output discipline + completion-marker conventions are at ../../reference/shared-preamble.md#output-contract-reminders.

Invocation Modes

InvocationAction
{{command_prefix}}benchmark <component>Harvest + synthesize a single component
{{command_prefix}}benchmark --wave <N>Run a full wave (1 = Inputs, 2 = Containers, etc.)
{{command_prefix}}benchmark --listShow corpus coverage - which specs exist, which are pending
{{command_prefix}}benchmark --refresh <component>Re-harvest a spec (for design-system version bumps)

Single-Component Flow ({{command_prefix}}benchmark <component>)

  1. Check if spec exists - Glob("reference/components/<component>.md"). If found and --refresh was not passed, confirm before overwriting.

  2. Harvest - spawn component-benchmark-harvester with required-reading @connections/design-corpora.md, target component, raw-output path .planning/benchmarks/raw/<component>.md, and a salvage hint to .planning/research/impeccable-salvage/. Acceptance: raw harvest exists with ≥4 source sections.

  3. Synthesize - spawn component-benchmark-synthesizer with required-reading @.planning/benchmarks/raw/<component>.md, @reference/components/TEMPLATE.md, @reference/anti-patterns.md. Output: reference/components/<component>.md. Also update reference/components/README.md (add entry in correct category). Acceptance: spec ≤350 lines, cites ≥4 systems, follows TEMPLATE.md sections, has a WAI-ARIA keyboard contract + a failing-example block.

  4. Report - print spec path, line count, systems cited.

Wave Mode ({{command_prefix}}benchmark --wave <N>)

Read the wave definition from reference/components/README.md and run each component sequentially (not parallel - each harvest is network-bound and the raw files are large).

WaveComponents
1button, input, select-combobox, checkbox, radio, switch, link, label
2card, modal-dialog, drawer, popover, tooltip, accordion, tabs

Print progress per component: [N/total] harvesting <component>…

List Mode ({{command_prefix}}benchmark --list)

Read reference/components/README.md and diff against reference/components/*.md files. Print a table with columns: Component, Status, Wave, Lines.

Refresh Mode ({{command_prefix}}benchmark --refresh <component>)

Same as single-component flow but skips the "already exists" guard. Use when a design system ships a breaking update to a component's spec.

Source List

../../connections/design-corpora.md - 18 design systems with canonical URLs, licensing, and fallback chain (canonical → archive.org → Refero MCP → Pinterest MCP).

Output Artifacts

ArtifactPurpose
.planning/benchmarks/raw/<component>.mdRaw multi-source harvest (input only)
reference/components/<component>.mdCanonical spec (distributed with plugin)
reference/components/README.mdCorpus index (updated by synthesizer)

BENCHMARK COMPLETE

Frequently asked questions

What does the Gdd Benchmark AI skill do?

Harvest and synthesize per-component design benchmarks from 18 design systems and produce canonical component specs at `reference/components/<name>.md`. Use when adding a new component spec, running a benchmark wave, listing corpus coverage, or refreshing a spec after a design-system version bump.

Why use Gdd Benchmark on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/benchmark. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gdd Benchmark?

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 Gdd Benchmark?

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

Is the Gdd Benchmark AI skill free?

Yes. It is published on GitHub by hegemonart 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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