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Reference Intelligence

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phazurlabs
reference-intelligence

Sources REAL design references instead of recalling them — award-winning live sites from Awwwards, app screens and flows from Mobbin (MCP), motion recipes from MotionSites (MCP) — captures them, measures their design DNA (type, color, spacing, grid, radius, motion stack), and synthesizes a Reference Board in .sumi/refs/ that every MAKE and REVIEW command builds and grades against. Use when the user mentions: inspiration, inspo, references, reference sites, moodboard, Awwwards, SOTD, site of the day, Mobbin, MotionSites, 'make it look like', 'sites like', 'award-winning', 'best in class', examples of, what are the best sites for, reference board, steal like an artist, benchmark against real sites.

Overview

Publisherphazurlabs
Repositorysumi
Skill namereference-intelligence
Stars
58
Forks
11
Bundled files
6
LicenseApache-2.0
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Reference Intelligence 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/phazurlabs/sumi/tree/main/skills/reference-intelligence
  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/phazurlabs/sumi.git /tmp/sumi
mkdir -p .claude/skills
cp -r /tmp/sumi/skills/reference-intelligence .claude/skills/reference-intelligence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reference Intelligence 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 Reference Intelligence 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 Reference Intelligence 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.

Reference Intelligence — look before you design

Every other Sumi skill encodes taste from memory. This one goes and looks. A senior studio doesn't design a fintech landing page from recollection; it pulls ten current winners onto a wall, pulls them apart, agrees what to steal, builds, then pins the build next to the wall and asks whether it belongs there.

That loop has four steps and Sumi owns all of them:

StepWhat happensWhere it lives
ScoutFind 8-12 candidates from real sourcesscripts/awwwards.mjs, Mobbin MCP, MotionSites MCP
CaptureScreenshot the LIVE sites and measure their DNAscripts/capture.mjs → .sumi/refs/<slug>/
BoardSynthesize what to steal / avoid into one direction.sumi/refs/board.md + board.json
CompareGrade a build side by side with the boardcritic agent, /grade, /roast

Source routing

The ask is…Primary sourceSecondary
Marketing site, landing page, portfolio, brand, campaignAwwwardsMotionSites for motion recipes
App screen, dashboard, settings, product UIMobbinAwwwards app-style / ui-design tags
A flow (onboarding, checkout, signup, paywall, search)Mobbin (flows)—
Motion, scroll storytelling, WebGL, transitionsAwwwards (gsap, three-js, webgl, transitions, scrolling)MotionSites
"Make it look like "That URL, straight into capture.mjsAwwwards category of that site

Adapters, one per source:

  • references/awwwards-adapter.md — listings, the 192 category tags, sector → tag map, etiquette
  • references/mobbin-adapter.md — MCP auth, query shapes, what may be stored
  • references/motionsites-adapter.md — prompt library, free-tier limit, upgrade message rule
  • references/dna-schema.md — what capture.mjs measures and how to read it
  • references/board-synthesis.md — combining references without cloning one; board.json schema
  • references/comparison-protocol.md — how the critic grades a build against the board

Non-negotiables

  1. Real, not recalled. A reference must be a URL that was fetched this session (or a cached fetch under .sumi/refs/), a Mobbin result from the MCP, or a MotionSites prompt. Never present a site as an Awwwards winner from memory.
  2. Capture the live site, not the thumbnail. Awwwards and Mobbin are indexes. The DNA comes from the site itself.
  3. One script call, not click-by-click browsing. Capture runs as ONE node scripts/capture.mjs invocation over all picks. Never drive puppeteer/Chrome MCP step by step to study a site.
  4. Small and on-demand. 8-12 candidates per run, 3-5 kept. No bulk crawling of Awwwards; reuse a cached listing under .sumi/refs/ if it is under 7 days old.
  5. Steal traits, never layouts. The board takes at most ONE signature trait from each reference (see board-synthesis.md). A build that is recognisably one reference is a fail.
  6. Screenshots stay local. Third-party captures are study material. Suggest adding .sumi/refs/**/*.jpg to .gitignore; never publish or deploy them.
  7. The user curates. Present the shortlist and let them keep 3-5. Art direction is a choice, not a computation.

Running the scripts

Scripts live in the plugin, not the project. Resolve the plugin root once:

bash
SUMI="${CLAUDE_PLUGIN_ROOT:-$(dirname "$(dirname "$(find ~/.claude/plugins -path '*sumi*/scripts/capture.mjs' -print -quit)")")}"
node "$SUMI/scripts/awwwards.mjs" technology --awarded --limit 10 --out .sumi/refs/awwwards-technology.json
node "$SUMI/scripts/capture.mjs" --from .sumi/refs/awwwards-technology.json --pick slug-a,slug-b,slug-c
node "$SUMI/scripts/capture.mjs" https://linear.app https://stripe.com   # direct URLs

capture.mjs needs puppeteer-core (resolved from the project, $SUMI_PUPPETEER_DIR, or known sibling projects) and a local Chrome. It takes ~40-60s per site (desktop + phone, scroll steps). Run it in the background for more than 3 sites.

Output: .sumi/refs/

.sumi/refs/
├── awwwards-<listing>.json   # scout results (cache)
├── mobbin.json               # Mobbin picks: app, screen/flow, url, why
├── motion.json               # MotionSites prompts used
├── <slug>/dna.json           # measured DNA, desktop + phone
├── <slug>/desktop-0..3.jpg   # scroll-step captures
├── <slug>/phone-0..2.jpg
├── board.md                  # human-readable Reference Board
└── board.json                # machine-readable direction every command consumes

board.json is the contract. /style turns its direction into tokens and its references into the references array of .sumi/style.json (per design-memory, only /style writes that file). /page, /screen and /component build against it; /grade and /roast compare against it.

Related skills

  • visual-design-mastery — the 10-dimension scoring the comparison uses
  • screen-flow-patterns — the screen/flow taxonomy used to phrase Mobbin queries
  • sector-style-intelligence — sector norms; its "Inspiration Links" seed Mobbin queries
  • interaction-motion-design, animation-recipe-library — translating captured motion stacks into code

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 Reference Intelligence AI skill do?

Sources REAL design references instead of recalling them — award-winning live sites from Awwwards, app screens and flows from Mobbin (MCP), motion recipes from MotionSites (MCP) — captures them, measures their design DNA (type, color, spacing, grid, radius, motion stack), and synthesizes a Reference Board in .sumi/refs/ that every MAKE and REVIEW command builds and grades against. Use when the user mentions: inspiration, inspo, references, reference sites, moodboard, Awwwards, SOTD, site of the day, Mobbin, MotionSites, 'make it look like', 'sites like', 'award-winning', 'best in class', ex...

Why use Reference Intelligence on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/phazurlabs/sumi/tree/main/skills/reference-intelligence. 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 Reference Intelligence?

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 Reference Intelligence?

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

Is the Reference Intelligence AI skill free?

Yes. It is published on GitHub by phazurlabs under the Apache-2.0 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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